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Differential Equations

1. Introduction and Classification

1.1 Basic Definitions

A differential equation (DE) is an equation involving an unknown function and its derivatives. An ordinary differential equation (ODE) involves a function of one variable and its ordinary Derivatives. A partial differential equation (PDE) involves a function of several variables and Its partial derivatives.

1.2 Classification of ODEs

An ODE is:

  • Ordinary vs. partial: depends on whether partial derivatives appear.
  • Order: the highest derivative that appears.
  • Linear vs. nonlinear: linear if the unknown function and its derivatives appear linearly.
  • Homogeneous vs. nonhomogeneous: for linear ODEs, homogeneous if the forcing term is zero.

1.3 Initial and Boundary Value Problems

An initial value problem (IVP) specifies the value of the function (and possibly its Derivatives) at a single point. A boundary value problem (BVP) specifies conditions at two or More points.

1.4 Examples from Physics and Biology

Differential equations arise throughout the natural sciences. A few canonical examples:

  1. Newton”s law of cooling. The temperature T(t)T(t) of a body in a medium at temperature TmT_m satisfies dTdt=k(TTm)\frac{dT}{dt} = -k(T - T_m)A first-order linear ODE.

  2. Harmonic oscillator. A mass on a spring with damping obeys md2xdt2+cdxdt+kx=F(t)m\frac{d^2 x}{dt^2} + c\frac{dx}{dt} + kx = F(t)A second-order linear ODE.

  3. Logistic population growth. dPdt=rP(1PK)\frac{dP}{dt} = rP\left(1 - \frac{P}{K}\right)A first-order nonlinear (Bernoulli) ODE.

  4. Lotka-Volterra predator-prey model. dxdt=x(αβy)\frac{dx}{dt} = x(\alpha - \beta y), dydt=y(γ+δx)\frac{dy}{dt} = y(-\gamma + \delta x)A coupled nonlinear system.

  5. RC circuit. The charge q(t)q(t) on a capacitor satisfies Rdqdt+qC=V(t)R\frac{dq}{dt} + \frac{q}{C} = V(t) a first-order linear ODE.

  6. Heat equation. The temperature u(x,t)u(x, t) in a rod satisfies ut=α2uxxu_t = \alpha^2 u_{xx}A second-order linear PDE.

  7. Wave equation. The displacement u(x,t)u(x, t) of a string satisfies utt=c2uxxu_{tt} = c^2 u_{xx}A second-order linear PDE.

  8. Laplace’s equation. The steady-state temperature satisfies uxx+uyy=0u_{xx} + u_{yy} = 0A second-order linear PDE.

1.5 Classification Tree

Differential Equations
├── ODE (one independent variable)
│ ├── By order
│ │ ├── First-order: y' = f(x, y)
│ │ ├── Second-order: y'' = f(x, y, y')
│ │ └── n-th order: y^(n) = f(x, y, ..., y^(n-1))
│ ├── By linearity
│ │ ├── Linear: a_n(x)y^(n) + ... + a_0(x)y = g(x)
│ │ │ ├── Homogeneous (g = 0)
│ │ │ └── Nonhomogeneous (g ≠ 0)
│ │ └── Nonlinear (y or derivatives appear nonlinearly)
│ └── By coefficients
│ ├── Constant coefficient
│ └── Variable coefficient
└── PDE (multiple independent variables)
├── Elliptic: B² - 4AC < 0 (e.g., Laplace)
├── Parabolic: B² - 4AC = 0 (e.g., Heat)
└── Hyperbolic: B² - 4AC > 0 (e.g., Wave)

1.6 Worked Example: Classifying ODEs

Problem. Classify each equation by order, linearity, and homogeneity (if linear).

(a) y+3y+2y=sinxy'' + 3y' + 2y = \sin x

(b) (y)2+y=0(y')^2 + y = 0

(c) x2y+xy+(x21)y=0x^2 y'' + xy' + (x^2 - 1)y = 0

(d) 2ux2+2uy2=0\frac{\partial^2 u}{\partial x^2} + \frac{\partial^2 u}{\partial y^2} = 0

Solution

(a) Second-order, linear, nonhomogeneous (forcing term sinx0\sin x \neq 0).

(b) First-order, nonlinear (the term (y)2(y')^2 is nonlinear in yy').

(c) Second-order, linear, homogeneous. This is Bessel’s equation of order 1.

(d) Second-order PDE, linear, homogeneous. This is Laplace’s equation; A=1A = 1, C=1C = 1, B=0B = 0 So B24AC=4<0B^2 - 4AC = -4 \lt 0 (elliptic). \blacksquare

2. First-Order ODEs

2.1 Separable Equations

A first-order ODE dydx=f(x,y)\frac{dy}{dx} = f(x, y) is separable if f(x,y)=g(x)h(y)f(x, y) = g(x)h(y).

dydx=g(x)h(y)    dyh(y)=g(x)dx\frac{dy}{dx} = g(x)h(y) \implies \frac{dy}{h(y)} = g(x)\, dx

Integrating both sides: dyh(y)=g(x)dx+C\int \frac{dy}{h(y)} = \int g(x)\, dx + C.

Example. Solve dydx=xy\frac{dy}{dx} = xy.

Separating: dyy=xdx\frac{dy}{y} = x\, dx. Integrating: lny=x22+C\ln|y| = \frac{x^2}{2} + C. Thus y=Cex2/2y = Ce^{x^2/2} where C0C \neq 0Plus the trivial solution y=0y = 0.

2.2 Linear First-Order Equations

A linear first-order ODE has the form

dydx+P(x)y=Q(x)\frac{dy}{dx} + P(x)y = Q(x)

Theorem 2.1 (Integrating Factor). The solution is

y(x)=eP(x)dx(Q(x)eP(x)dxdx+C)y(x) = e^{-\int P(x)\, dx}\left(\int Q(x) e^{\int P(x)\, dx}\, dx + C\right)

Proof. Multiply both sides by μ(x)=eP(x)dx\mu(x) = e^{\int P(x)\, dx}:

ddx(μy)=μdydx+μPy=μdydx+μy=μ(dydx+Py)=μQ\frac{d}{dx}(\mu y) = \mu \frac{dy}{dx} + \mu P y = \mu \frac{dy}{dx} + \mu' y = \mu\left(\frac{dy}{dx} + Py\right) = \mu Q

Integrating: μy=μQdx+C\mu y = \int \mu Q\, dx + C. Solving for yy gives the result. \blacksquare

2.3 Worked Example: Linear Equation

Problem. Solve y+2xy=x2y' + \frac{2}{x}y = x^2 for x>0x > 0.

Solution. P(x)=2/xP(x) = 2/x, Q(x)=x2Q(x) = x^2.

μ(x)=e2/xdx=e2lnx=x2\mu(x) = e^{\int 2/x\, dx} = e^{2\ln x} = x^2.

y=x2(x2x2dx+C)=x2(x55+C)=x35+Cx2y = x^{-2}\left(\int x^2 \cdot x^2\, dx + C\right) = x^{-2}\left(\frac{x^5}{5} + C\right) = \frac{x^3}{5} + \frac{C}{x^2}. \blacksquare

2.4 Exact Equations

The ODE M(x,y)dx+N(x,y)dy=0M(x, y)\, dx + N(x, y)\, dy = 0 is exact if My=Nx\frac{\partial M}{\partial y} = \frac{\partial N}{\partial x}.

When exact, there exists Ψ(x,y)\Psi(x, y) such that Ψx=M\frac{\partial \Psi}{\partial x} = M and Ψy=N\frac{\partial \Psi}{\partial y} = NAnd the solution is Ψ(x,y)=C\Psi(x, y) = C.

Theorem 2.2. If MM and NN have continuous partial derivatives on a connected domain DDThen Mdx+Ndy=0M\, dx + N\, dy = 0 is exact if and only if My=NxM_y = N_x.

Proof. If exact, M=ΨxM = \Psi_x and N=ΨyN = \Psi_ySo My=Ψxy=Ψyx=NxM_y = \Psi_{xy} = \Psi_{yx} = N_x by Clairaut. Conversely, if My=NxM_y = N_xDefine Ψ(x,y)=x0xM(t,y)dt+y0yN(x0,s)ds\Psi(x, y) = \int_{x_0}^x M(t, y)\, dt + \int_{y_0}^y N(x_0, s)\, ds. Then Ψx=M(x,y)\Psi_x = M(x, y) and Ψy=x0xMy(t,y)dt+N(x0,y)=x0xNx(t,y)dt+N(x0,y)=N(x,y)N(x0,y)+N(x0,y)=N(x,y)\Psi_y = \int_{x_0}^x M_y(t, y)\, dt + N(x_0, y) = \int_{x_0}^x N_x(t, y)\, dt + N(x_0, y) = N(x, y) - N(x_0, y) + N(x_0, y) = N(x, y). \blacksquare

2.5 Worked Example: Exact Equation

Problem. Solve (2xy+3)dx+(x21)dy=0(2xy + 3)\, dx + (x^2 - 1)\, dy = 0.

Solution. M=2xy+3M = 2xy + 3, N=x21N = x^2 - 1. Check: My=2x=NxM_y = 2x = N_x. Exact.

Ψx=2xy+3    Ψ=x2y+3x+h(y)\Psi_x = 2xy + 3 \implies \Psi = x^2 y + 3x + h(y).

Ψy=x2+h(y)=x21    h(y)=1    h(y)=y\Psi_y = x^2 + h'(y) = x^2 - 1 \implies h'(y) = -1 \implies h(y) = -y.

Solution: x2y+3xy=Cx^2 y + 3x - y = C. \blacksquare

2.6 Integrating Factors for Non-Exact Equations

If MyNxM_y \neq N_xOne can sometimes find an integrating factor μ(x,y)\mu(x, y) such that (μM)y=(μN)x(\mu M)_y = (\mu N)_x.

Case 1: If MyNxN\frac{M_y - N_x}{N} depends only on xxThen μ(x)=eMyNxNdx\mu(x) = e^{\int \frac{M_y - N_x}{N}\, dx}.

Case 2: If NxMyM\frac{N_x - M_y}{M} depends only on yyThen μ(y)=eNxMyMdy\mu(y) = e^{\int \frac{N_x - M_y}{M}\, dy}.

2.7 Bernoulli Equations

A Bernoulli equation has the form

dydx+P(x)y=Q(x)yn\frac{dy}{dx} + P(x)y = Q(x)y^n

Where n0,1n \neq 0, 1. The substitution v=y1nv = y^{1-n} transforms it into a linear equation:

dvdx+(1n)P(x)v=(1n)Q(x)\frac{dv}{dx} + (1 - n)P(x)v = (1 - n)Q(x)

Example. Solve y+y=y2exy' + y = y^2 e^x.

Here n=2n = 2So set v=y1v = y^{-1}. Then v=y2yv' = -y^{-2}y'And the equation becomes vv=exv' - v = -e^x. Integrating factor: exe^{-x}. So (vex)=1(v e^{-x})' = -1Giving vex=x+Cve^{-x} = -x + C, v=xex+Cexv = -xe^x + Ce^x And y=1/(Cx)exy = 1/(C - x)e^x.

2.8 Existence and Uniqueness

Theorem 2.3 (Picard-Lindelöf). If ff and f/y\partial f/\partial y are continuous on a rectangle Containing (x0,y0)(x_0, y_0)Then the IVP y=f(x,y)y' = f(x, y), y(x0)=y0y(x_0) = y_0 has a unique solution in some Neighbourhood of x0x_0.

2.9 Substitution Methods

Several substitutions reduce specific equations to separable or linear form:

  1. Homogeneous equations: y=f(y/x)y' = f(y/x). Set v=y/xv = y/x.
  2. Equations of the form y=f(ax+by+c)y' = f(ax + by + c): set v=ax+by+cv = ax + by + c.

2.10 Homogeneous Equations

An ODE of the form dydx=F(yx)\frac{dy}{dx} = F\left(\frac{y}{x}\right) is called homogeneous (not to be Confused with the linearity sense). The substitution v=y/xv = y/xI.e., y=vxy = vxGives y=v+xvy' = v + xv' So the equation becomes:

v+xdvdx=F(v)    xdvdx=F(v)vv + x\frac{dv}{dx} = F(v) \implies x\frac{dv}{dx} = F(v) - v

This is separable: dvF(v)v=dxx\frac{dv}{F(v) - v} = \frac{dx}{x}.

2.11 Worked Example: Homogeneous Equation

Problem. Solve y=x2+y2xyy' = \frac{x^2 + y^2}{xy}.

Solution

Solution. Rewrite as y=1+(y/x)2y/xy' = \frac{1 + (y/x)^2}{y/x}. This is homogeneous with F(v)=1+v2vF(v) = \frac{1 + v^2}{v}.

Set y=vxy = vx: v+xv=1+v2v=v+1vv + xv' = \frac{1 + v^2}{v} = v + \frac{1}{v}.

So xv=1vxv' = \frac{1}{v}Giving vdv=dxxv\, dv = \frac{dx}{x}.

Integrating: v22=lnx+C\frac{v^2}{2} = \ln|x| + C. Since v=y/xv = y/x:

y22x2=lnx+C    y2=2x2(lnx+C)\frac{y^2}{2x^2} = \ln|x| + C \implies y^2 = 2x^2(\ln|x| + C). \blacksquare

2.12 Riccati Equations

A Riccati equation has the form

dydx=q0(x)+q1(x)y+q2(x)y2\frac{dy}{dx} = q_0(x) + q_1(x)y + q_2(x)y^2

If a particular solution y1(x)y_1(x) is known, the substitution y=y1+1vy = y_1 + \frac{1}{v} reduces the Riccati equation to a linear first-order equation in vv:

dvdx=(q1+2q2y1)vq2\frac{dv}{dx} = -(q_1 + 2q_2 y_1)v - q_2

Example. Solve y=1+x22xy+y2y' = 1 + x^2 - 2xy + y^2 given that y1=xy_1 = x is a particular solution.

Substituting y=x+1/vy = x + 1/v: y=1v/v2y' = 1 - v'/v^2. The equation becomes

1v/v2=1+x22x(x+1/v)+(x+1/v)21 - v'/v^2 = 1 + x^2 - 2x(x + 1/v) + (x + 1/v)^2

1v/v2=1+x22x22x/v+x2+2x/v+1/v21 - v'/v^2 = 1 + x^2 - 2x^2 - 2x/v + x^2 + 2x/v + 1/v^2

1v/v2=1+1/v21 - v'/v^2 = 1 + 1/v^2

v/v2=1/v2    v=1-v'/v^2 = 1/v^2 \implies v' = -1

So v=x+Cv = -x + CAnd y=x+1Cxy = x + \frac{1}{C - x}.

2.13 Worked Example: Newton’s Law of Cooling

Problem. A body at 90°C90\degree\mathrm{C} is placed in a room at 20°C20\degree\mathrm{C}. After 10 Minutes, its temperature is 60°C60\degree\mathrm{C}. When will it reach 30°C30\degree\mathrm{C}?

Solution

Solution. Newton’s law of cooling: dTdt=k(T20)\frac{dT}{dt} = -k(T - 20), T(0)=90T(0) = 90.

This is separable: dTT20=kdt\frac{dT}{T - 20} = -k\, dt.

ln(T20)=kt+C    T=20+Cekt\ln(T - 20) = -kt + C \implies T = 20 + Ce^{-kt}.

T(0)=90    C=70T(0) = 90 \implies C = 70So T=20+70ektT = 20 + 70e^{-kt}.

T(10)=60    60=20+70e10k    e10k=4/7T(10) = 60 \implies 60 = 20 + 70e^{-10k} \implies e^{-10k} = 4/7.

k=110ln(4/7)=ln(7/4)10k = -\frac{1}{10}\ln(4/7) = \frac{\ln(7/4)}{10}.

For T=30T = 30: 30=20+70ekt    ekt=1/7    t=ln7k=10ln7ln(7/4)30 = 20 + 70e^{-kt} \implies e^{-kt} = 1/7 \implies t = \frac{\ln 7}{k} = \frac{10 \ln 7}{\ln(7/4)}.

Numerically: t101.9460.559634.8t \approx \frac{10 \cdot 1.946}{0.5596} \approx 34.8 minutes. \blacksquare

2.14 Worked Example: Mixing Problem

Problem. A tank contains 100 L of brine with 20 kg of salt. Fresh water enters at 3 L/min and The mixture leaves at 3 L/min. Find the amount of salt after 30 minutes.

Solution

Solution. Let Q(t)Q(t) be the amount of salt (kg) at time tt (min).

Rate of change: dQdt=rate  inrate  out=03Q100\frac{dQ}{dt} = \mathrm{rate}\; in - \mathrm{rate}\; out = 0 - 3 \cdot \frac{Q}{100}.

dQdt=3Q100\frac{dQ}{dt} = -\frac{3Q}{100}, Q(0)=20Q(0) = 20.

This is separable: dQQ=3100dt\frac{dQ}{Q} = -\frac{3}{100}\, dt.

lnQ=3t100+C    Q=Ce3t/100\ln Q = -\frac{3t}{100} + C \implies Q = Ce^{-3t/100}.

Q(0)=20    Q=20e3t/100Q(0) = 20 \implies Q = 20e^{-3t/100}.

At t=30t = 30: Q(30)=20e0.9200.40668.13Q(30) = 20e^{-0.9} \approx 20 \cdot 0.4066 \approx 8.13 kg. \blacksquare

2.15 Worked Example: Integrating Factor

Problem. Solve (3xy+2y2)dx+(x2+2xy)dy=0(3xy + 2y^2)\, dx + (x^2 + 2xy)\, dy = 0.

Solution

Solution. M=3xy+2y2M = 3xy + 2y^2, N=x2+2xyN = x^2 + 2xy.

My=3x+4yM_y = 3x + 4y, Nx=2x+2yN_x = 2x + 2y. Since MyNxM_y \neq N_xNot exact.

Check Case 1: MyNxN=x+2yx2+2xy=x+2yx(x+2y)=1x\frac{M_y - N_x}{N} = \frac{x + 2y}{x^2 + 2xy} = \frac{x + 2y}{x(x + 2y)} = \frac{1}{x}.

This depends only on xxSo μ(x)=e1/xdx=x\mu(x) = e^{\int 1/x\, dx} = x.

Multiply: (3x2y+2xy2)dx+(x3+2x2y)dy=0(3x^2y + 2xy^2)\, dx + (x^3 + 2x^2y)\, dy = 0.

M~=3x2y+2xy2\tilde{M} = 3x^2y + 2xy^2, N~=x3+2x2y\tilde{N} = x^3 + 2x^2y.

M~y=3x2+4xy=N~x\tilde{M}_y = 3x^2 + 4xy = \tilde{N}_x. Now exact.

Ψx=3x2y+2xy2    Ψ=x3y+x2y2+h(y)\Psi_x = 3x^2y + 2xy^2 \implies \Psi = x^3y + x^2y^2 + h(y).

Ψy=x3+2x2y+h(y)=x3+2x2y    h(y)=0    h(y)=0\Psi_y = x^3 + 2x^2y + h'(y) = x^3 + 2x^2y \implies h'(y) = 0 \implies h(y) = 0.

Solution: x3y+x2y2=Cx^3y + x^2y^2 = C. \blacksquare

2.16 Orthogonal Trajectories

Given a one-parameter family of curves F(x,y,C)=0F(x, y, C) = 0The orthogonal trajectories are curves That intersect every member of the family at right angles. To find them:

  1. Find the differential equation dydx=f(x,y)\frac{dy}{dx} = f(x, y) of the given family.
  2. Replace dydx\frac{dy}{dx} with dxdy-\frac{dx}{dy} (equivalently, negate the slope).
  3. Solve the new ODE.

Example. Find the orthogonal trajectories of y=Cx2y = Cx^2.

dydx=2Cx=2yx\frac{dy}{dx} = 2Cx = \frac{2y}{x}.

Orthogonal trajectories satisfy dydx=x2y\frac{dy}{dx} = -\frac{x}{2y}.

Separating: 2ydy=xdx2y\, dy = -x\, dx. Integrating: y2=x22+Cy^2 = -\frac{x^2}{2} + COr x22+y2=C\frac{x^2}{2} + y^2 = C. These are ellipses.

2.17 Common Pitfalls for First-Order ODEs

:::caution Common Pitfall When separating variables, dividing by h(y)h(y) can lose solutions where h(y)=0h(y) = 0. Always check whether h(y)=0h(y) = 0 yields valid solutions before dividing. :::

:::caution Common Pitfall Not every first-order ODE falls into a standard category. Equations like y=ex2+sin(y2)y' = e^{x^2} + \sin(y^2) cannot be solved by elementary methods and require numerical Techniques. :::

3. Second-Order Linear ODEs

3.1 General Theory

A second-order linear ODE has the form

y+p(x)y+q(x)y=g(x)y'' + p(x)y' + q(x)y = g(x)

Theorem 3.1. If y1y_1 and y2y_2 are solutions of the homogeneous equation y+py+qy=0y'' + py' + qy = 0 Then c1y1+c2y2c_1 y_1 + c_2 y_2 is also a solution (superposition principle).

Theorem 3.2 (Wronskian Criterion). Two solutions y1,y2y_1, y_2 of the homogeneous equation form a fundamental set (i.e., span all solutions) if and only if their Wronskian is non-zero:

W(y1,y2)(x)=y1y2y1y20W(y_1, y_2)(x) = \begin{vmatrix} y_1 & y_2 \\ y_1' & y_2' \end{vmatrix} \neq 0

Abel’s identity states that W(x)=W(x0)ex0xp(t)dtW(x) = W(x_0) e^{-\int_{x_0}^x p(t)\, dt}.

Theorem 3.3. The general solution of y+py+qy=gy'' + py' + qy = g is y=yh+ypy = y_h + y_pWhere yhy_h is the General solution of the homogeneous equation and ypy_p is any particular solution.

3.2 Homogeneous Equations with Constant Coefficients

For y+ay+by=0y'' + ay' + by = 0 with a,ba, b constants, try y=erxy = e^{rx}:

r2+ar+b=0r^2 + ar + b = 0

Case 1: Two distinct real roots r1r2r_1 \neq r_2. yh=c1er1x+c2er2xy_h = c_1 e^{r_1 x} + c_2 e^{r_2 x}.

Case 2: Repeated root rr. yh=c1erx+c2xerxy_h = c_1 e^{rx} + c_2 x e^{rx}.

Case 3: Complex conjugate roots r=α±iβr = \alpha \pm i\beta. yh=eαx(c1cos(βx)+c2sin(βx))y_h = e^{\alpha x}(c_1 \cos(\beta x) + c_2 \sin(\beta x)).

3.3 Worked Example: Homogeneous Equation

Problem. Solve y5y+6y=0y'' - 5y' + 6y = 0 with y(0)=1y(0) = 1, y(0)=0y'(0) = 0.

Solution. Characteristic equation: r25r+6=(r2)(r3)=0r^2 - 5r + 6 = (r-2)(r-3) = 0. Roots: r=2,3r = 2, 3.

yh=c1e2x+c2e3xy_h = c_1 e^{2x} + c_2 e^{3x}.

y(0)=c1+c2=1y(0) = c_1 + c_2 = 1. y(0)=2c1+3c2=0y'(0) = 2c_1 + 3c_2 = 0. Solving: c1=3c_1 = 3, c2=2c_2 = -2.

y=3e2x2e3xy = 3e^{2x} - 2e^{3x}. \blacksquare

3.4 Worked Example: Complex Roots

Problem. Solve y+2y+5y=0y'' + 2y' + 5y = 0 with y(0)=1y(0) = 1, y(0)=0y'(0) = 0.

Solution

Solution. Characteristic equation: r2+2r+5=0r^2 + 2r + 5 = 0.

r=2±4202=2±162=1±2ir = \frac{-2 \pm \sqrt{4 - 20}}{2} = \frac{-2 \pm \sqrt{-16}}{2} = -1 \pm 2i.

So α=1\alpha = -1, β=2\beta = 2.

y=ex(c1cos(2x)+c2sin(2x))y = e^{-x}(c_1 \cos(2x) + c_2 \sin(2x)).

y(0)=c1=1y(0) = c_1 = 1.

y=ex(cos(2x)+c2sin(2x))+ex(2sin(2x)+2c2cos(2x))y' = -e^{-x}(\cos(2x) + c_2 \sin(2x)) + e^{-x}(-2\sin(2x) + 2c_2 \cos(2x)).

y(0)=1+2c2=0    c2=1/2y'(0) = -1 + 2c_2 = 0 \implies c_2 = 1/2.

y=ex(cos(2x)+12sin(2x))y = e^{-x}\left(\cos(2x) + \frac{1}{2}\sin(2x)\right). \blacksquare

3.5 Worked Example: Repeated Roots

Problem. Solve y4y+4y=0y'' - 4y' + 4y = 0 with y(0)=1y(0) = 1, y(0)=3y'(0) = 3.

Solution

Solution. Characteristic equation: r24r+4=(r2)2=0r^2 - 4r + 4 = (r - 2)^2 = 0. Repeated root r=2r = 2.

y=c1e2x+c2xe2xy = c_1 e^{2x} + c_2 xe^{2x}.

y(0)=c1=1y(0) = c_1 = 1.

y=2c1e2x+c2e2x+2c2xe2xy' = 2c_1 e^{2x} + c_2 e^{2x} + 2c_2 xe^{2x}.

y(0)=2c1+c2=3    2+c2=3    c2=1y'(0) = 2c_1 + c_2 = 3 \implies 2 + c_2 = 3 \implies c_2 = 1.

y=e2x+xe2x=e2x(1+x)y = e^{2x} + xe^{2x} = e^{2x}(1 + x). \blacksquare

3.6 Nonhomogeneous Equations: Undetermined Coefficients

For equations y+ay+by=g(x)y'' + ay' + by = g(x) where g(x)g(x) is a polynomial, exponential, sine, cosine, or Products of these, guess the form of ypy_p and solve for coefficients.

g(x)g(x)Guess for ypy_p
Pn(x)P_n(x)Anxn++A0A_n x^n + \cdots + A_0
eaxe^{ax}AeaxAe^{ax}
sin(bx)\sin(bx) or cos(bx)\cos(bx)Asin(bx)+Bcos(bx)A\sin(bx) + B\cos(bx)
eaxPn(x)e^{ax} P_n(x)eax(Anxn++A0)e^{ax}(A_n x^n + \cdots + A_0)
eaxsin(bx)e^{ax}\sin(bx) or eaxcos(bx)e^{ax}\cos(bx)eax(Asin(bx)+Bcos(bx))e^{ax}(A\sin(bx) + B\cos(bx))

Rule. If any term of the guess is a solution of the homogeneous equation, multiply by xx (or x2x^2 if already multiplied by xx).

3.7 Worked Example: Undetermined Coefficients

Problem. Solve yy=2exy'' - y = 2e^x.

Solution. Homogeneous: r21=0r^2 - 1 = 0Roots ±1\pm 1. yh=c1ex+c2exy_h = c_1 e^x + c_2 e^{-x}.

Since exe^x is a homogeneous solution, guess yp=Axexy_p = Axe^x. yp=Aex+Axexy_p' = Ae^x + Axe^x yp=2Aex+Axexy_p'' = 2Ae^x + Axe^x. ypyp=(2Aex+Axex)Axex=2Aex=2exy_p'' - y_p = (2Ae^x + Axe^x) - Axe^x = 2Ae^x = 2e^xSo A=1A = 1.

y=c1ex+c2ex+xexy = c_1 e^x + c_2 e^{-x} + xe^x. \blacksquare

3.8 Worked Example: Undetermined Coefficients with Polynomial

Problem. Solve y+3y+2y=x2+1y'' + 3y' + 2y = x^2 + 1.

Solution

Solution. Homogeneous: r2+3r+2=(r+1)(r+2)=0r^2 + 3r + 2 = (r+1)(r+2) = 0Roots 1,2-1, -2.

yh=c1ex+c2e2xy_h = c_1 e^{-x} + c_2 e^{-2x}.

Guess yp=Ax2+Bx+Cy_p = Ax^2 + Bx + C. Then yp=2Ax+By_p' = 2Ax + B, yp=2Ay_p'' = 2A.

Substituting: 2A+3(2Ax+B)+2(Ax2+Bx+C)=x2+12A + 3(2Ax + B) + 2(Ax^2 + Bx + C) = x^2 + 1.

2A+6Ax+3B+2Ax2+2Bx+2C=x2+12A + 6Ax + 3B + 2Ax^2 + 2Bx + 2C = x^2 + 1.

Matching coefficients:

  • x2x^2: 2A=1    A=1/22A = 1 \implies A = 1/2
  • xx: 6A+2B=0    3+2B=0    B=3/26A + 2B = 0 \implies 3 + 2B = 0 \implies B = -3/2
  • Constant: 2A+3B+2C=1    19/2+2C=1    2C=9/2    C=9/42A + 3B + 2C = 1 \implies 1 - 9/2 + 2C = 1 \implies 2C = 9/2 \implies C = 9/4

yp=x223x2+94y_p = \frac{x^2}{2} - \frac{3x}{2} + \frac{9}{4}.

y=c1ex+c2e2x+x223x2+94y = c_1 e^{-x} + c_2 e^{-2x} + \frac{x^2}{2} - \frac{3x}{2} + \frac{9}{4}. \blacksquare

3.8b Worked Example: Undetermined Coefficients with Product

Problem. Solve y+2y+y=3exsinxy'' + 2y' + y = 3e^{-x}\sin x.

Solution

Solution. Homogeneous: r2+2r+1=(r+1)2=0r^2 + 2r + 1 = (r+1)^2 = 0. Repeated root r=1r = -1.

yh=c1ex+c2xexy_h = c_1 e^{-x} + c_2 xe^{-x}.

The forcing is exsinxe^{-x}\sin xSo guess yp=ex(Asinx+Bcosx)y_p = e^{-x}(A\sin x + B\cos x).

yp=ex(Asinx+Bcosx)+ex(AcosxBsinx)=ex((AB)cosx(A+B)sinx)y_p' = -e^{-x}(A\sin x + B\cos x) + e^{-x}(A\cos x - B\sin x) = e^{-x}((A - B)\cos x - (A + B)\sin x).

yp=ex((AB)cosx(A+B)sinx)+ex((AB)sinx(A+B)cosx)y_p'' = -e^{-x}((A - B)\cos x - (A + B)\sin x) + e^{-x}(-(A - B)\sin x - (A + B)\cos x)

=ex(2Acosx+2Bsinx)= e^{-x}(-2A\cos x + 2B\sin x).

yp+2yp+yp=ex(2Acosx+2Bsinx)+2ex((AB)cosx(A+B)sinx)+ex(Asinx+Bcosx)y_p'' + 2y_p' + y_p = e^{-x}(-2A\cos x + 2B\sin x) + 2e^{-x}((A - B)\cos x - (A + B)\sin x) + e^{-x}(A\sin x + B\cos x)

=ex[(2A+2A2B+B)cosx+(2B2A2B+A)sinx]= e^{-x}[(-2A + 2A - 2B + B)\cos x + (2B - 2A - 2B + A)\sin x]

=ex[(B)cosx+(A)sinx]= e^{-x}[(-B)\cos x + (-A)\sin x].

Setting equal to 3exsinx3e^{-x}\sin x: B=0-B = 0 and A=3-A = 3So A=3A = -3, B=0B = 0.

y=c1ex+c2xex3exsinxy = c_1 e^{-x} + c_2 xe^{-x} - 3e^{-x}\sin x. \blacksquare

### 3.9 Resonance

Consider the forced harmonic oscillator

y+ω02y=F0cos(ωt)y'' + \omega_0^2 y = F_0 \cos(\omega t)

Case 1: ωω0\omega \neq \omega_0 (Non-resonant). The particular solution is yp=F0ω02ω2cos(ωt)y_p = \frac{F_0}{\omega_0^2 - \omega^2}\cos(\omega t)With bounded amplitude.

Case 2: ω=ω0\omega = \omega_0 (Resonant). Since cos(ω0t)\cos(\omega_0 t) is a homogeneous solution, Guess yp=Atsin(ω0t)y_p = At\sin(\omega_0 t). Substituting:

yp+ω02yp=2Aω0cos(ω0t)Aω02tsin(ω0t)+Aω02tsin(ω0t)=2Aω0cos(ω0t)y_p'' + \omega_0^2 y_p = 2A\omega_0 \cos(\omega_0 t) - A\omega_0^2 t\sin(\omega_0 t) + A\omega_0^2 t\sin(\omega_0 t) = 2A\omega_0 \cos(\omega_0 t)

Setting equal to F0cos(ω0t)F_0 \cos(\omega_0 t): A=F02ω0A = \frac{F_0}{2\omega_0}.

yp=F02ω0tsin(ω0t)y_p = \frac{F_0}{2\omega_0} t \sin(\omega_0 t)

The amplitude grows linearly with tt --- this is resonance. Physically, the system absorbs energy From the periodic forcing at its natural frequency, causing unbounded oscillations.

Worked Example. Solve y+9y=6cos(3t)y'' + 9y = 6\cos(3t), y(0)=0y(0) = 0, y(0)=0y'(0) = 0.

Solution

Solution. ω0=3\omega_0 = 3, ω=3\omega = 3So this is the resonant case.

Homogeneous: r2+9=0r^2 + 9 = 0, r=±3ir = \pm 3i. yh=c1cos(3t)+c2sin(3t)y_h = c_1 \cos(3t) + c_2 \sin(3t).

yp=623tsin(3t)=tsin(3t)y_p = \frac{6}{2 \cdot 3} t\sin(3t) = t\sin(3t).

y=c1cos(3t)+c2sin(3t)+tsin(3t)y = c_1 \cos(3t) + c_2 \sin(3t) + t\sin(3t).

y(0)=c1=0y(0) = c_1 = 0.

y=3c2cos(3t)+sin(3t)+3tcos(3t)y' = 3c_2 \cos(3t) + \sin(3t) + 3t\cos(3t).

y(0)=3c2=0    c2=0y'(0) = 3c_2 = 0 \implies c_2 = 0.

y=tsin(3t)y = t\sin(3t). \blacksquare

3.10 Variation of Parameters

Theorem 3.4 (Variation of Parameters). For y+p(x)y+q(x)y=g(x)y'' + p(x)y' + q(x)y = g(x)Let y1,y2y_1, y_2 be a Fundamental set of solutions of the homogeneous equation. Then a particular solution is

yp=y1y2gWdx+y2y1gWdxy_p = -y_1 \int \frac{y_2 g}{W}\, dx + y_2 \int \frac{y_1 g}{W}\, dx

Where W=W(y1,y2)=y1y2y2y1W = W(y_1, y_2) = y_1 y_2' - y_2 y_1'.

Proof. Seek yp=u1(x)y1(x)+u2(x)y2(x)y_p = u_1(x)y_1(x) + u_2(x)y_2(x). Impose the constraint u1y1+u2y2=0u_1'y_1 + u_2'y_2 = 0. Then yp=u1y1+u2y2y_p' = u_1 y_1' + u_2 y_2' and yp=u1y1+u1y1+u2y2+u2y2y_p'' = u_1' y_1' + u_1 y_1'' + u_2' y_2' + u_2 y_2''. Substituting into the ODE: (u1y1+u2y2)+u1(y1+py1+qy1)+u2(y2+py2+qy2)=g(u_1'y_1' + u_2'y_2') + u_1(y_1'' + py_1' + qy_1) + u_2(y_2'' + py_2' + qy_2) = g. Since y1,y2y_1, y_2 satisfy the homogeneous equation, this reduces to u1y1+u2y2=gu_1'y_1' + u_2'y_2' = g. Together With u1y1+u2y2=0u_1'y_1 + u_2'y_2 = 0Solving gives the formulas above. \blacksquare

3.11 Worked Example: Variation of Parameters

Problem. Solve y+y=tanxy'' + y = \tan x using variation of parameters.

Solution. y1=cosxy_1 = \cos x, y2=sinxy_2 = \sin x. W=cosxcosxsinx(sinx)=1W = \cos x \cdot \cos x - \sin x \cdot (-\sin x) = 1.

u1=y2gW=sinxtanx=sin2xcosx=(1cos2x)/cosx=secx+cosxu_1' = -\frac{y_2 g}{W} = -\sin x \tan x = -\frac{\sin^2 x}{\cos x} = -(1 - \cos^2 x)/\cos x = -\sec x + \cos x.

u1=lnsecx+tanx+sinxu_1 = -\ln|\sec x + \tan x| + \sin x.

u2=y1gW=cosxtanx=sinxu_2' = \frac{y_1 g}{W} = \cos x \tan x = \sin x.

u2=cosxu_2 = -\cos x.

yp=(lnsecx+tanx+sinx)cosx+(cosx)sinx=cosxlnsecx+tanxy_p = (-\ln|\sec x + \tan x| + \sin x)\cos x + (-\cos x)\sin x = -\cos x \ln|\sec x + \tan x|.

y=c1cosx+c2sinxcosxlnsecx+tanxy = c_1 \cos x + c_2 \sin x - \cos x \ln|\sec x + \tan x|. \blacksquare

3.12 Reduction of Order

Theorem 3.5. Given one solution y1(x)y_1(x) of y+p(x)y+q(x)y=0y'' + p(x)y' + q(x)y = 0A second linearly Independent solution is obtained by setting y2=y1ep(x)dxy12dxy_2 = y_1 \int \frac{e^{-\int p(x)\, dx}}{y_1^2}\, dx.

Proof. Seek y2=v(x)y1(x)y_2 = v(x) y_1(x). Then y2=vy1+vy1y_2' = v'y_1 + vy_1' and y2=vy1+2vy1+vy1y_2'' = v''y_1 + 2v'y_1' + vy_1''. Substituting into the ODE:

vy1+2vy1+vy1+p(vy1+vy1)+qvy1=0v''y_1 + 2v'y_1' + vy_1'' + p(v'y_1 + vy_1') + qvy_1 = 0

vy1+v(2y1+py1)+v(y1+py1+qy1)=0v''y_1 + v'(2y_1' + py_1) + v(y_1'' + py_1' + qy_1) = 0

Since y1y_1 satisfies the ODE, the coefficient of vv vanishes:

vy1+v(2y1+py1)=0v''y_1 + v'(2y_1' + py_1) = 0

Let w=vw = v'. Then wy1+w(2y1+py1)=0w'y_1 + w(2y_1' + py_1) = 0A separable first-order ODE:

ww=2y1+py1y1=2y1y1p\frac{w'}{w} = -\frac{2y_1' + py_1}{y_1} = -2\frac{y_1'}{y_1} - p

lnw=2lny1pdx    w=epdxy12\ln w = -2\ln y_1 - \int p\, dx \implies w = \frac{e^{-\int p\, dx}}{y_1^2}

Since w=vw = v'We obtain the result. \blacksquare

Worked Example. Given that y1=exy_1 = e^x solves y2y+y=0y'' - 2y' + y = 0Find a second solution.

Solution

Solution. Here p(x)=2p(x) = -2So epdx=e2xe^{-\int p\, dx} = e^{2x}.

y2=exe2xe2xdx=ex1dx=xexy_2 = e^x \int \frac{e^{2x}}{e^{2x}}\, dx = e^x \int 1\, dx = xe^x.

This gives yh=c1ex+c2xexy_h = c_1 e^x + c_2 xe^xConsistent with the repeated-root case (r=1r = 1 with Multiplicity 2). \blacksquare

3.13 Euler-Cauchy Equations

An Euler-Cauchy (equidimensional) equation has the form

x2y+axy+by=0,x>0x^2 y'' + axy' + by = 0, \quad x > 0

The substitution y=xry = x^r gives the characteristic equation

r(r1)+ar+b=r2+(a1)r+b=0r(r - 1) + ar + b = r^2 + (a - 1)r + b = 0

Case 1: Two distinct real roots r1r2r_1 \neq r_2. yh=c1xr1+c2xr2y_h = c_1 x^{r_1} + c_2 x^{r_2}.

Case 2: Repeated root rr. yh=c1xr+c2xrlnxy_h = c_1 x^r + c_2 x^r \ln x.

Case 3: Complex roots r=α±iβr = \alpha \pm i\beta. yh=xα(c1cos(βlnx)+c2sin(βlnx))y_h = x^{\alpha}(c_1 \cos(\beta \ln x) + c_2 \sin(\beta \ln x)).

3.14 Worked Example: Euler-Cauchy Equation

Problem. Solve x2y3xy+4y=0x^2 y'' - 3xy' + 4y = 0.

Solution

Solution. Try y=xry = x^r: r(r1)3r+4=r24r+4=(r2)2=0r(r-1) - 3r + 4 = r^2 - 4r + 4 = (r-2)^2 = 0.

Repeated root r=2r = 2.

y=c1x2+c2x2lnxy = c_1 x^2 + c_2 x^2 \ln x. \blacksquare

Worked Example. Solve x2y+xy+y=0x^2 y'' + xy' + y = 0.

Solution

Solution. r(r1)+r+1=r2+1=0r(r-1) + r + 1 = r^2 + 1 = 0. Roots r=±ir = \pm i.

Here α=0\alpha = 0, β=1\beta = 1.

y=c1cos(lnx)+c2sin(lnx)y = c_1 \cos(\ln x) + c_2 \sin(\ln x). \blacksquare

3.15 Higher-Order Linear ODEs

For y(n)+an1y(n1)++a1y+a0y=0y^{(n)} + a_{n-1}y^{(n-1)} + \cdots + a_1 y' + a_0 y = 0:

  • Characteristic equation rn+an1rn1++a0=0r^n + a_{n-1}r^{n-1} + \cdots + a_0 = 0.
  • For root rr of multiplicity mm: include erx,xerx,,xm1erxe^{rx}, xe^{rx}, \ldots, x^{m-1}e^{rx}.
  • For complex roots α±iβ\alpha \pm i\beta of multiplicity mm: include eαxxkcos(βx)e^{\alpha x} x^k \cos(\beta x) and eαxxksin(βx)e^{\alpha x} x^k \sin(\beta x) for k=0,,m1k = 0, \ldots, m - 1.

3.16 Spring-Mass-Damper Systems

A mass mm on a spring with spring constant kk and damping coefficient ccSubject to external force F(t)F(t)Satisfies

mx+cx+kx=F(t)mx'' + cx' + kx = F(t)

Dividing by mm and setting ω0=k/m\omega_0 = \sqrt{k/m}, γ=c/(2m)\gamma = c/(2m):

x+2γx+ω02x=F(t)mx'' + 2\gamma x' + \omega_0^2 x = \frac{F(t)}{m}

The homogeneous solution depends on the discriminant γ2ω02\gamma^2 - \omega_0^2:

ConditionTypeHomogeneous Solution
γ2<ω02\gamma^2 \lt \omega_0^2Underdampedeγt(c1cos(ωdt)+c2sin(ωdt))e^{-\gamma t}(c_1 \cos(\omega_d t) + c_2 \sin(\omega_d t)), ωd=ω02γ2\omega_d = \sqrt{\omega_0^2 - \gamma^2}
γ2=ω02\gamma^2 = \omega_0^2Criticaleγt(c1+c2t)e^{-\gamma t}(c_1 + c_2 t)
γ2>ω02\gamma^2 > \omega_0^2Overdampedc1er1t+c2er2tc_1 e^{r_1 t} + c_2 e^{r_2 t}, r1,2=γ±γ2ω02r_{1,2} = -\gamma \pm \sqrt{\gamma^2 - \omega_0^2}

3.17 Common Pitfalls for Second-Order ODEs

:::caution Common Pitfall When using undetermined coefficients, always check whether your guess Overlaps with the homogeneous solution. For y4y=e2xy'' - 4y = e^{2x}Guessing yp=Ae2xy_p = Ae^{2x} fails Because e2xe^{2x} satisfies the homogeneous equation. You must use yp=Axe2xy_p = Axe^{2x} instead. :::

:::caution Common Pitfall For Euler-Cauchy equations, the substitution y=xry = x^r only works for x>0x > 0. For x<0x < 0Substitute x=etx = -e^t or use y=(x)ry = (-x)^r. :::

:::caution Common Pitfall Variation of parameters always works but can lead to difficult integrals. If the forcing term g(x)g(x) is a polynomial, exponential, sine, or cosine (or products of these), Prefer undetermined coefficients --- it is much faster. :::

3.18 Abel’s Identity (Proof)

Theorem 3.6 (Abel’s Identity). If y1,y2y_1, y_2 are solutions of y+p(x)y+q(x)y=0y'' + p(x)y' + q(x)y = 0 Then their Wronskian satisfies

W(x)=W(x0)ex0xp(t)dtW(x) = W(x_0) e^{-\int_{x_0}^x p(t)\, dt}

Proof. Since y1,y2y_1, y_2 satisfy the ODE:

y1=py1qy1y_1'' = -py_1' - qy_1 and y2=py2qy2y_2'' = -py_2' - qy_2.

W=y1y2+y1y2y1y2y1y2W' = y_1 y_2'' + y_1' y_2' - y_1'' y_2 - y_1' y_2'

=y1(py2qy2)(py1qy1)y2= y_1(-py_2' - qy_2) - (-py_1' - qy_1)y_2

=p(y1y2y1y2)=pW= -p(y_1 y_2' - y_1' y_2) = -pW.

So W+pW=0W' + pW = 0Giving W=CepdxW = Ce^{-\int p\, dx}And evaluating at x0x_0 gives the result. \blacksquare

Corollary. W(x)W(x) is either identically zero or never zero.

3.19 Worked Example: Variation of Parameters (Second Example)

Problem. Solve y4y=xexy'' - 4y = xe^x using variation of parameters.

Solution

Solution. Homogeneous: r24=0r^2 - 4 = 0, r=±2r = \pm 2. y1=e2xy_1 = e^{2x}, y2=e2xy_2 = e^{-2x}.

W=e2x(2e2x)e2x(2e2x)=4W = e^{2x}(-2e^{-2x}) - e^{-2x}(2e^{2x}) = -4.

u1=y2gW=e2xxex4=xex4u_1' = -\frac{y_2 g}{W} = -\frac{e^{-2x} \cdot xe^x}{-4} = \frac{xe^{-x}}{4}.

u1=14xexdx=14(xexex)+C1=(x+1)ex4u_1 = \frac{1}{4}\int xe^{-x}\, dx = \frac{1}{4}(-xe^{-x} - e^{-x}) + C_1 = -\frac{(x+1)e^{-x}}{4}.

u2=y1gW=e2xxex4=xe3x4u_2' = \frac{y_1 g}{W} = \frac{e^{2x} \cdot xe^x}{-4} = -\frac{xe^{3x}}{4}.

u2=14xe3xdx=14(xe3x3e3x9)+C2=(3x1)e3x36u_2 = -\frac{1}{4}\int xe^{3x}\, dx = -\frac{1}{4}\left(\frac{xe^{3x}}{3} - \frac{e^{3x}}{9}\right) + C_2 = -\frac{(3x - 1)e^{3x}}{36}.

yp=u1y1+u2y2=(x+1)ex4e2x+((3x1)e3x36)e2xy_p = u_1 y_1 + u_2 y_2 = -\frac{(x+1)e^{-x}}{4} \cdot e^{2x} + \left(-\frac{(3x-1)e^{3x}}{36}\right) \cdot e^{-2x}

=(x+1)ex4(3x1)ex36=ex(9(x+1)363x136)=ex(9x93x+136)=(x+2)ex9= -\frac{(x+1)e^x}{4} - \frac{(3x-1)e^x}{36} = e^x\left(-\frac{9(x+1)}{36} - \frac{3x - 1}{36}\right) = e^x\left(\frac{-9x - 9 - 3x + 1}{36}\right) = -\frac{(x + 2)e^x}{9}.

y=c1e2x+c2e2x(x+2)ex9y = c_1 e^{2x} + c_2 e^{-2x} - \frac{(x+2)e^x}{9}. \blacksquare

## 4. Systems of ODEs

4.1 First-Order Linear Systems

A system of first-order linear ODEs can be written in matrix form:

x=Ax+f(t)\mathbf{x}' = A\mathbf{x} + \mathbf{f}(t)

Where AA is an n×nn \times n matrix and x,fRn\mathbf{x}, \mathbf{f} \in \mathbb{R}^n.

4.2 Homogeneous Systems with Constant Coefficients

For x=Ax\mathbf{x}' = A\mathbf{x}Try x=veλt\mathbf{x} = \mathbf{v}e^{\lambda t}:

λv=Av\lambda \mathbf{v} = A\mathbf{v}

So λ\lambda is an eigenvalue of AA and v\mathbf{v} is the corresponding eigenvector.

Case 1: AA has nn distinct real eigenvalues. The general solution is

x=c1v1eλ1t++cnvneλnt\mathbf{x} = c_1 \mathbf{v}_1 e^{\lambda_1 t} + \cdots + c_n \mathbf{v}_n e^{\lambda_n t}

Case 2: AA has a repeated eigenvalue λ\lambda with algebraic multiplicity mm and geometric Multiplicity k<mk \lt m. Include terms involving tjeλtt^j e^{\lambda t} where generalized Eigenvectors fill out the solution space.

Case 3: Complex eigenvalues λ=α±iβ\lambda = \alpha \pm i\beta with eigenvector v=a±ib\mathbf{v} = \mathbf{a} \pm i\mathbf{b}. The real solutions are eαt(acos(βt)bsin(βt))e^{\alpha t}(\mathbf{a}\cos(\beta t) - \mathbf{b}\sin(\beta t)) and eαt(asin(βt)+bcos(βt))e^{\alpha t}(\mathbf{a}\sin(\beta t) + \mathbf{b}\cos(\beta t)).

4.3 The Matrix Exponential

Definition. eAt=k=0Aktkk!e^{At} = \sum_{k=0}^{\infty} \frac{A^k t^k}{k!}.

Theorem 4.1. The solution to x=Ax\mathbf{x}' = A\mathbf{x} with x(0)=x0\mathbf{x}(0) = \mathbf{x}_0 is x(t)=eAtx0\mathbf{x}(t) = e^{At}\mathbf{x}_0.

Proposition 4.2. If AA is diagonalizable as A=PDP1A = PDP^{-1}Then eAt=PeDtP1e^{At} = Pe^{Dt}P^{-1} Where eDt=diag(eλ1t,,eλnt)e^{Dt} = \mathrm{diag}(e^{\lambda_1 t}, \ldots, e^{\lambda_n t}).

4.4 Worked Example: Distinct Real Eigenvalues

Problem. Solve x=(0123)x\mathbf{x}' = \begin{pmatrix} 0 & 1 \\ -2 & -3 \end{pmatrix}\mathbf{x}.

Solution. Characteristic equation: det(AλI)=λ2+3λ+2=(λ+1)(λ+2)=0\det(A - \lambda I) = \lambda^2 + 3\lambda + 2 = (\lambda + 1)(\lambda + 2) = 0. Eigenvalues: λ1=1\lambda_1 = -1, λ2=2\lambda_2 = -2.

For λ1=1\lambda_1 = -1: (A+I)v=(1122)v=0(A + I)\mathbf{v} = \begin{pmatrix} 1 & 1 \\ -2 & -2 \end{pmatrix}\mathbf{v} = \mathbf{0}. v1=(11)\mathbf{v}_1 = \begin{pmatrix} 1 \\ -1 \end{pmatrix}.

For λ2=2\lambda_2 = -2: (A+2I)v=(2121)v=0(A + 2I)\mathbf{v} = \begin{pmatrix} 2 & 1 \\ -2 & -1 \end{pmatrix}\mathbf{v} = \mathbf{0}. v2=(12)\mathbf{v}_2 = \begin{pmatrix} 1 \\ -2 \end{pmatrix}.

x(t)=c1(11)et+c2(12)e2t\mathbf{x}(t) = c_1 \begin{pmatrix} 1 \\ -1 \end{pmatrix} e^{-t} + c_2 \begin{pmatrix} 1 \\ -2 \end{pmatrix} e^{-2t}. \blacksquare

4.5 Worked Example: Complex Eigenvalues

Problem. Solve x=(0210)x\mathbf{x}' = \begin{pmatrix} 0 & -2 \\ 1 & 0 \end{pmatrix}\mathbf{x}.

Solution

Solution. det(AλI)=λ2+2=0\det(A - \lambda I) = \lambda^2 + 2 = 0. Eigenvalues: λ=±i2\lambda = \pm i\sqrt{2}.

For λ=i2\lambda = i\sqrt{2}: (i221i2)v=0\begin{pmatrix} -i\sqrt{2} & -2 \\ 1 & -i\sqrt{2} \end{pmatrix}\mathbf{v} = \mathbf{0}.

From the first row: i2v12v2=0-i\sqrt{2}\, v_1 - 2v_2 = 0So v2=i22v1v_2 = -\frac{i\sqrt{2}}{2}v_1.

With v1=2v_1 = 2: v=(20)+i(02)\mathbf{v} = \begin{pmatrix} 2 \\ 0 \end{pmatrix} + i\begin{pmatrix} 0 \\ -\sqrt{2} \end{pmatrix}.

So a=(20)\mathbf{a} = \begin{pmatrix} 2 \\ 0 \end{pmatrix}, b=(02)\mathbf{b} = \begin{pmatrix} 0 \\ -\sqrt{2} \end{pmatrix}.

x(t)=c1[acos(2t)bsin(2t)]+c2[asin(2t)+bcos(2t)]\mathbf{x}(t) = c_1\left[\mathbf{a}\cos(\sqrt{2}\, t) - \mathbf{b}\sin(\sqrt{2}\, t)\right] + c_2\left[\mathbf{a}\sin(\sqrt{2}\, t) + \mathbf{b}\cos(\sqrt{2}\, t)\right]

=c1(2cos(2t)2sin(2t))+c2(2sin(2t)2cos(2t))= c_1 \begin{pmatrix} 2\cos(\sqrt{2}\, t) \\ \sqrt{2}\sin(\sqrt{2}\, t) \end{pmatrix} + c_2 \begin{pmatrix} 2\sin(\sqrt{2}\, t) \\ -\sqrt{2}\cos(\sqrt{2}\, t) \end{pmatrix}. \blacksquare

4.6 Worked Example: Repeated Eigenvalues

Problem. Solve x=(2114)x\mathbf{x}' = \begin{pmatrix} 2 & 1 \\ -1 & 4 \end{pmatrix}\mathbf{x}.

Solution

Solution. det(AλI)=(2λ)(4λ)+1=λ26λ+9=(λ3)2=0\det(A - \lambda I) = (2 - \lambda)(4 - \lambda) + 1 = \lambda^2 - 6\lambda + 9 = (\lambda - 3)^2 = 0.

Repeated eigenvalue λ=3\lambda = 3 with algebraic multiplicity 2.

(A3I)=(1111)(A - 3I) = \begin{pmatrix} -1 & 1 \\ -1 & 1 \end{pmatrix}.

Eigenvector: (11)\begin{pmatrix} 1 \\ 1 \end{pmatrix}. Only one eigenvector (geometric multiplicity 1), so we need a generalized eigenvector.

Find w\mathbf{w} such that (A3I)w=v1=(11)(A - 3I)\mathbf{w} = \mathbf{v}_1 = \begin{pmatrix} 1 \\ 1 \end{pmatrix}:

(1111)(w1w2)=(11)\begin{pmatrix} -1 & 1 \\ -1 & 1 \end{pmatrix}\begin{pmatrix} w_1 \\ w_2 \end{pmatrix} = \begin{pmatrix} 1 \\ 1 \end{pmatrix}

w1+w2=1-w_1 + w_2 = 1. Choose w1=0w_1 = 0Then w2=1w_2 = 1. So w=(01)\mathbf{w} = \begin{pmatrix} 0 \\ 1 \end{pmatrix}.

x(t)=c1(11)e3t+c2[(11)te3t+(01)e3t]\mathbf{x}(t) = c_1 \begin{pmatrix} 1 \\ 1 \end{pmatrix} e^{3t} + c_2 \left[\begin{pmatrix} 1 \\ 1 \end{pmatrix} t e^{3t} + \begin{pmatrix} 0 \\ 1 \end{pmatrix} e^{3t}\right]

=e3t[c1(11)+c2(tt+1)]= e^{3t}\left[c_1 \begin{pmatrix} 1 \\ 1 \end{pmatrix} + c_2 \begin{pmatrix} t \\ t + 1 \end{pmatrix}\right]. \blacksquare

4.7 Fundamental Matrix

Definition. A fundamental matrix Φ(t)\Phi(t) for the system x=Ax\mathbf{x}' = A\mathbf{x} is an n×nn \times n matrix whose columns form a fundamental set of solutions.

Proposition 4.3. Φ(t)\Phi(t) satisfies Φ=AΦ\Phi' = A\PhiAnd the general solution is x(t)=Φ(t)c\mathbf{x}(t) = \Phi(t)\mathbf{c} for cRn\mathbf{c} \in \mathbb{R}^n.

Proposition 4.4. The matrix exponential eAte^{At} is a fundamental matrix with eA0=Ie^{A \cdot 0} = I. Any fundamental matrix can be written as Φ(t)=eAtΦ(0)\Phi(t) = e^{At}\Phi(0).

4.8 Matrix Exponential Properties

Theorem 4.5. The matrix exponential satisfies:

  1. eA0=Ie^{A \cdot 0} = I
  2. ddteAt=AeAt=eAtA\frac{d}{dt}e^{At} = Ae^{At} = e^{At}A
  3. eAteAs=eA(t+s)e^{At}e^{As} = e^{A(t+s)}
  4. (eAt)1=eAt(e^{At})^{-1} = e^{-At}
  5. If AB=BAAB = BAThen eA+B=eAeBe^{A+B} = e^A e^B

Proof of (1). eA0=k=0Ak0kk!=Ie^{A \cdot 0} = \sum_{k=0}^{\infty} \frac{A^k 0^k}{k!} = I. \blacksquare

Proof of (2). ddteAt=k=1Aktk1(k1)!=Aj=0Ajtjj!=AeAt\frac{d}{dt}e^{At} = \sum_{k=1}^{\infty} \frac{A^k t^{k-1}}{(k-1)!} = A\sum_{j=0}^{\infty} \frac{A^j t^j}{j!} = Ae^{At}. Since AA commutes with itself, AeAt=eAtAAe^{At} = e^{At}A. \blacksquare

Proof of (4). From (3) with s=ts = -t: eAteAt=eA(tt)=e0=Ie^{At}e^{-At} = e^{A(t-t)} = e^0 = I. \blacksquare

4.9 Phase Portrait Analysis for 2D Systems

For the linear system x=Ax\mathbf{x}' = A\mathbf{x} with AA a 2×22 \times 2 matrix, the qualitative Behaviour near the origin is determined by the eigenvalues:

EigenvaluesPhase PortraitStability
λ1,λ2<0\lambda_1, \lambda_2 \lt 0Real, distinctStable nodeAsymptotically stable
λ1,λ2>0\lambda_1, \lambda_2 > 0Real, distinctUnstable nodeUnstable
λ1<0<λ2\lambda_1 \lt 0 \lt \lambda_2Saddle pointUnstable
λ=α±iβ\lambda = \alpha \pm i\beta, α<0\alpha \lt 0Stable spiralAsymptotically stable
λ=α±iβ\lambda = \alpha \pm i\beta, α>0\alpha > 0Unstable spiralUnstable
λ=±iβ\lambda = \pm i\betaCenter(Marginally) stable

Remark. The trace-determinant plane provides a convenient classification. Let τ=tr(A)\tau = \mathrm{tr}(A) and Δ=det(A)\Delta = \det(A). The eigenvalues satisfy λ2τλ+Δ=0\lambda^2 - \tau\lambda + \Delta = 0So:

λ=τ±τ24Δ2\lambda = \frac{\tau \pm \sqrt{\tau^2 - 4\Delta}}{2}

  • τ24Δ>0\tau^2 - 4\Delta > 0: real eigenvalues (node or saddle)
  • τ24Δ<0\tau^2 - 4\Delta \lt 0: complex eigenvalues (spiral or center)
  • τ24Δ=0\tau^2 - 4\Delta = 0: repeated eigenvalues (proper or improper node)

Stability is determined by the sign of τ\tau: stable if τ<0\tau \lt 0Unstable if τ>0\tau > 0.

4.10 Nonhomogeneous Systems

For x=Ax+f(t)\mathbf{x}' = A\mathbf{x} + \mathbf{f}(t)If Φ(t)\Phi(t) is a fundamental matrix for the Homogeneous system, the general solution is

x(t)=Φ(t)c+Φ(t)Φ1(s)f(s)ds\mathbf{x}(t) = \Phi(t)\mathbf{c} + \Phi(t)\int \Phi^{-1}(s)\mathbf{f}(s)\, ds

Worked Example. Solve x=(1002)x+(et0)\mathbf{x}' = \begin{pmatrix} 1 & 0 \\ 0 & 2 \end{pmatrix}\mathbf{x} + \begin{pmatrix} e^t \\ 0 \end{pmatrix}.

Solution

Solution. Eigenvalues: 11 and 22. Φ(t)=(et00e2t)\Phi(t) = \begin{pmatrix} e^t & 0 \\ 0 & e^{2t} \end{pmatrix}.

Φ1(s)=(es00e2s)\Phi^{-1}(s) = \begin{pmatrix} e^{-s} & 0 \\ 0 & e^{-2s} \end{pmatrix}.

Φ1(s)f(s)=(es00e2s)(es0)=(10)\Phi^{-1}(s)\mathbf{f}(s) = \begin{pmatrix} e^{-s} & 0 \\ 0 & e^{-2s} \end{pmatrix}\begin{pmatrix} e^s \\ 0 \end{pmatrix} = \begin{pmatrix} 1 \\ 0 \end{pmatrix}.

Φ1(s)f(s)ds=(t0)\int \Phi^{-1}(s)\mathbf{f}(s)\, ds = \begin{pmatrix} t \\ 0 \end{pmatrix}.

xp=Φ(t)(t0)=(tet0)\mathbf{x}_p = \Phi(t)\begin{pmatrix} t \\ 0 \end{pmatrix} = \begin{pmatrix} te^t \\ 0 \end{pmatrix}.

x(t)=c1(et0)+c2(0e2t)+(tet0)\mathbf{x}(t) = c_1 \begin{pmatrix} e^t \\ 0 \end{pmatrix} + c_2 \begin{pmatrix} 0 \\ e^{2t} \end{pmatrix} + \begin{pmatrix} te^t \\ 0 \end{pmatrix}. \blacksquare

## 5. Laplace Transforms

5.1 Definition

The Laplace transform of f(t)f(t) (defined for t0t \geq 0) is

L{f(t)}=F(s)=0estf(t)dt\mathcal{L}\{f(t)\} = F(s) = \int_0^{\infty} e^{-st} f(t)\, dt

The transform exists when ff is piecewise continuous on [0,)[0, \infty) and of exponential order: f(t)Meat|f(t)| \leq Me^{at} for some M,a>0M, a > 0.

5.2 Basic Properties

Theorem 5.1 (Linearity). L{af+bg}=aL{f}+bL{g}\mathcal{L}\{af + bg\} = a\mathcal{L}\{f\} + b\mathcal{L}\{g\}.

Theorem 5.2 (First Shifting). L{eatf(t)}=F(sa)\mathcal{L}\{e^{at}f(t)\} = F(s - a).

Theorem 5.3 (Second Shifting). L{uc(t)f(tc)}=ecsF(s)\mathcal{L}\{u_c(t)f(t - c)\} = e^{-cs}F(s)Where uc(t)u_c(t) is The unit step function.

Theorem 5.4 (Derivative). L{f(t)}=sF(s)f(0)\mathcal{L}\{f'(t)\} = sF(s) - f(0).

Theorem 5.5 (n-th Derivative). L{f(n)(t)}=snF(s)sn1f(0)f(n1)(0)\mathcal{L}\{f^{(n)}(t)\} = s^n F(s) - s^{n-1}f(0) - \cdots - f^{(n-1)}(0).

Theorem 5.6 (Integration). L{0tf(τ)dτ}=F(s)s\mathcal{L}\left\{\int_0^t f(\tau)\, d\tau\right\} = \frac{F(s)}{s}.

Theorem 5.7 (Convolution). L{fg}=F(s)G(s)\mathcal{L}\{f * g\} = F(s)G(s)Where (fg)(t)=0tf(τ)g(tτ)dτ(f * g)(t) = \int_0^t f(\tau)g(t - \tau)\, d\tau.

5.3 Proofs of Key Properties

Proof of Linearity. L{af+bg}=0est[af(t)+bg(t)]dt=a0estf(t)dt+b0estg(t)dt=aF(s)+bG(s)\mathcal{L}\{af + bg\} = \int_0^{\infty} e^{-st}[af(t) + bg(t)]\, dt = a\int_0^{\infty} e^{-st}f(t)\, dt + b\int_0^{\infty} e^{-st}g(t)\, dt = aF(s) + bG(s). \blacksquare

Proof of First Shifting Theorem. L{eatf(t)}=0esteatf(t)dt=0e(sa)tf(t)dt=F(sa)\mathcal{L}\{e^{at}f(t)\} = \int_0^{\infty} e^{-st} e^{at} f(t)\, dt = \int_0^{\infty} e^{-(s-a)t} f(t)\, dt = F(s - a). \blacksquare

Proof of Derivative Property. Integrating by parts with u=estu = e^{-st}, dv=f(t)dtdv = f'(t)\, dt:

L{f(t)}=[estf(t)]0+s0estf(t)dt=f(0)+sF(s)=sF(s)f(0)\mathcal{L}\{f'(t)\} = \left[e^{-st}f(t)\right]_0^{\infty} + s\int_0^{\infty} e^{-st}f(t)\, dt = -f(0) + sF(s) = sF(s) - f(0).

The boundary term vanishes at \infty because ff is of exponential order. \blacksquare

Proof of Integration Property. Let g(t)=0tf(τ)dτg(t) = \int_0^t f(\tau)\, d\tau. Then g(t)=f(t)g'(t) = f(t) and g(0)=0g(0) = 0. L{f(t)}=L{g(t)}=sL{g(t)}g(0)=sL{g(t)}\mathcal{L}\{f(t)\} = \mathcal{L}\{g'(t)\} = s\mathcal{L}\{g(t)\} - g(0) = s\mathcal{L}\{g(t)\}. Therefore L{g(t)}=F(s)/s\mathcal{L}\{g(t)\} = F(s)/s. \blacksquare

5.4 Common Laplace Transforms

f(t)f(t)F(s)=L{f(t)}F(s) = \mathcal{L}\{f(t)\}
111/s1/s
tnt^nn!/sn+1n!/s^{n+1}
eate^{at}1/(sa)1/(s - a)
tneatt^n e^{at}n!/(sa)n+1n!/(s - a)^{n+1}
sin(bt)\sin(bt)b/(s2+b2)b/(s^2 + b^2)
cos(bt)\cos(bt)s/(s2+b2)s/(s^2 + b^2)
eatsin(bt)e^{at}\sin(bt)b/((sa)2+b2)b/((s-a)^2 + b^2)
eatcos(bt)e^{at}\cos(bt)(sa)/((sa)2+b2)(s-a)/((s-a)^2 + b^2)
uc(t)u_c(t)ecs/se^{-cs}/s
δ(tc)\delta(t - c)ecse^{-cs}

5.5 Solving IVPs with Laplace Transforms

Procedure:

  1. Take the Laplace transform of both sides of the ODE.
  2. Solve the resulting algebraic equation for Y(s)=L{y(t)}Y(s) = \mathcal{L}\{y(t)\}.
  3. Find the inverse Laplace transform to obtain y(t)y(t).

5.6 Worked Example: Solving an IVP

Problem. Solve y+4y=sin(2t)y'' + 4y = \sin(2t), y(0)=0y(0) = 0, y(0)=0y'(0) = 0.

Solution. Taking Laplace transforms:

s2Y+4Y=2s2+4s^2 Y + 4Y = \frac{2}{s^2 + 4}

(s2+4)Y=2s2+4(s^2 + 4)Y = \frac{2}{s^2 + 4}

Y=2(s2+4)2Y = \frac{2}{(s^2 + 4)^2}

To invert, use the convolution theorem: L1{1s2+4}=12sin(2t)\mathcal{L}^{-1}\left\{\frac{1}{s^2 + 4}\right\} = \frac{1}{2}\sin(2t).

y(t)=12sin(2t)12sin(2t)=140tsin(2τ)sin(2(tτ))dτy(t) = \frac{1}{2}\sin(2t) * \frac{1}{2}\sin(2t) = \frac{1}{4}\int_0^t \sin(2\tau)\sin(2(t - \tau))\, d\tau

Using sinAsinB=12(cos(AB)cos(A+B))\sin A \sin B = \frac{1}{2}(\cos(A - B) - \cos(A + B)):

y(t)=180t[cos(4τ2t)cos(2t)]dτ=18[sin(4τ2t)4]0tt8cos(2t)y(t) = \frac{1}{8}\int_0^t [\cos(4\tau - 2t) - \cos(2t)]\, d\tau = \frac{1}{8}\left[\frac{\sin(4\tau - 2t)}{4}\right]_0^t - \frac{t}{8}\cos(2t)

=132[sin(2t)sin(2t)]t8cos(2t)=sin(2t)16tcos(2t)8= \frac{1}{32}[\sin(2t) - \sin(-2t)] - \frac{t}{8}\cos(2t) = \frac{\sin(2t)}{16} - \frac{t\cos(2t)}{8}

y(t)=sin(2t)2tcos(2t)16y(t) = \frac{\sin(2t) - 2t\cos(2t)}{16}. \blacksquare

5.6b Worked Example: Partial Fractions for Inverse Laplace

Problem. Find \mathcal{L}^{-1}\left\{\frac{1}{s(s^2 + 4)} Ight\}.

Solution

Solution. Partial fractions: 1s(s2+4)=As+Bs+Cs2+4\frac{1}{s(s^2 + 4)} = \frac{A}{s} + \frac{Bs + C}{s^2 + 4}.

1=A(s2+4)+(Bs+C)s=(A+B)s2+Cs+4A1 = A(s^2 + 4) + (Bs + C)s = (A + B)s^2 + Cs + 4A.

s2s^2: A+B=0A + B = 0, ss: C=0C = 0Constant: 4A=1    A=1/44A = 1 \implies A = 1/4, B=1/4B = -1/4.

1s(s2+4)=1/4ss/4s2+4\frac{1}{s(s^2 + 4)} = \frac{1/4}{s} - \frac{s/4}{s^2 + 4}.

f(t)=1414cos2tf(t) = \frac{1}{4} - \frac{1}{4}\cos 2t. \blacksquare

### 5.7 Convolution Theorem

Theorem 5.8 (Convolution). If F(s)=L{f(t)}F(s) = \mathcal{L}\{f(t)\} and G(s)=L{g(t)}G(s) = \mathcal{L}\{g(t)\} Then

L{fg}=F(s)G(s)\mathcal{L}\{f * g\} = F(s)G(s)

Where (fg)(t)=0tf(τ)g(tτ)dτ(f * g)(t) = \int_0^t f(\tau)g(t - \tau)\, d\tau.

Proof. F(s)G(s)=0esτf(τ)dτ0esug(u)duF(s)G(s) = \int_0^{\infty} e^{-s\tau} f(\tau)\, d\tau \cdot \int_0^{\infty} e^{-su} g(u)\, du

=00es(τ+u)f(τ)g(u)dudτ= \int_0^{\infty} \int_0^{\infty} e^{-s(\tau+u)} f(\tau) g(u)\, du\, d\tau

Substitute t=τ+ut = \tau + u, u=tτu = t - \tau. For fixed τ\tau, uu ranges from 00 to \inftySo tt ranges From τ\tau to \infty.

=0f(τ)[τestg(tτ)dt]dτ= \int_0^{\infty} f(\tau)\left[\int_{\tau}^{\infty} e^{-st} g(t - \tau)\, dt\right] d\tau

Swap the order of integration (Fubini):

=0est[0tf(τ)g(tτ)dτ]dt=L{(fg)(t)}= \int_0^{\infty} e^{-st}\left[\int_0^t f(\tau) g(t - \tau)\, d\tau\right] dt = \mathcal{L}\{(f * g)(t)\}. \blacksquare

5.8 Worked Example: Inverse Laplace Transform

Problem. Find L1{3s+1s2+4s+13}\mathcal{L}^{-1}\left\{\frac{3s + 1}{s^2 + 4s + 13}\right\}.

Solution

Solution. Complete the square: s2+4s+13=(s+2)2+9s^2 + 4s + 13 = (s + 2)^2 + 9.

3s+1s2+4s+13=3(s+2)5(s+2)2+9=3s+2(s+2)2+9533(s+2)2+9\frac{3s + 1}{s^2 + 4s + 13} = \frac{3(s + 2) - 5}{(s + 2)^2 + 9} = 3 \cdot \frac{s + 2}{(s + 2)^2 + 9} - \frac{5}{3} \cdot \frac{3}{(s + 2)^2 + 9}

Using the shifting theorem:

L1{s+2(s+2)2+9}=e2tcos(3t)\mathcal{L}^{-1}\left\{\frac{s + 2}{(s + 2)^2 + 9}\right\} = e^{-2t}\cos(3t)

L1{3(s+2)2+9}=e2tsin(3t)\mathcal{L}^{-1}\left\{\frac{3}{(s + 2)^2 + 9}\right\} = e^{-2t}\sin(3t)

f(t)=3e2tcos(3t)53e2tsin(3t)=e2t(3cos(3t)53sin(3t))f(t) = 3e^{-2t}\cos(3t) - \frac{5}{3}e^{-2t}\sin(3t) = e^{-2t}\left(3\cos(3t) - \frac{5}{3}\sin(3t)\right). \blacksquare

5.9 Worked Example: IVP with Discontinuous Forcing

Problem. Solve y+3y+2y=u2(t)y'' + 3y' + 2y = u_2(t), y(0)=0y(0) = 0, y(0)=0y'(0) = 0.

Solution

Solution. Take Laplace transforms:

s2Y+3sY+2Y=e2sss^2 Y + 3sY + 2Y = \frac{e^{-2s}}{s}

(s2+3s+2)Y=e2ss(s^2 + 3s + 2)Y = \frac{e^{-2s}}{s}

Y=e2ss(s+1)(s+2)Y = \frac{e^{-2s}}{s(s+1)(s+2)}

First find the partial fraction decomposition of 1s(s+1)(s+2)\frac{1}{s(s+1)(s+2)}:

1s(s+1)(s+2)=As+Bs+1+Cs+2\frac{1}{s(s+1)(s+2)} = \frac{A}{s} + \frac{B}{s+1} + \frac{C}{s+2}

1=A(s+1)(s+2)+Bs(s+2)+Cs(s+1)1 = A(s+1)(s+2) + Bs(s+2) + Cs(s+1)

s=0s = 0: 1=2A    A=1/21 = 2A \implies A = 1/2 s=1s = -1: 1=B    B=11 = -B \implies B = -1 s=2s = -2: 1=2C    C=1/21 = 2C \implies C = 1/2

So L1{1s(s+1)(s+2)}=12et+12e2t\mathcal{L}^{-1}\left\{\frac{1}{s(s+1)(s+2)}\right\} = \frac{1}{2} - e^{-t} + \frac{1}{2}e^{-2t}.

By the second shifting theorem:

y(t)=u2(t)[12e(t2)+12e2(t2)]y(t) = u_2(t)\left[\frac{1}{2} - e^{-(t-2)} + \frac{1}{2}e^{-2(t-2)}\right] for t0t \geq 0. \blacksquare

5.10 The Heaviside Function

The Heaviside (unit step) function is defined as

uc(t)={0t<c1tcu_c(t) = \begin{cases} 0 & t \lt c \\ 1 & t \geq c \end{cases}

It models a sudden switch being turned on at time t=ct = c. The second shifting theorem states:

L{uc(t)f(tc)}=ecsF(s)\mathcal{L}\{u_c(t)f(t - c)\} = e^{-cs}F(s)

Conversely, if Y(s)=ecsG(s)Y(s) = e^{-cs}G(s)Then y(t)=uc(t)g(tc)y(t) = u_c(t) \cdot g(t - c) where g=L1{G}g = \mathcal{L}^{-1}\{G\}.

5.11 The Dirac Delta Function

The Dirac delta function δ(tc)\delta(t - c) satisfies 0δ(tc)f(t)dt=f(c)\int_0^{\infty} \delta(t - c)f(t)\, dt = f(c) for continuous ff. It models an instantaneous Impulse.

L{δ(tc)}=ecs\mathcal{L}\{\delta(t - c)\} = e^{-cs}.

Properties:

  • δ(tc)=0\delta(t - c) = 0 for tct \neq c
  • δ(tc)dt=1\int_{-\infty}^{\infty} \delta(t - c)\, dt = 1
  • δ(tc)f(t)dt=f(c)\int_{-\infty}^{\infty} \delta(t - c)f(t)\, dt = f(c) (sifting property)

5.12 Worked Example: Impulse Response

Problem. An undamped spring-mass system (m=1m = 1, k=4k = 4) is struck by an impulse at t=0t = 0: y+4y=δ(t)y'' + 4y = \delta(t), y(0)=0y(0) = 0, y(0)=0y'(0) = 0. Find y(t)y(t).

Solution

Solution. Taking Laplace transforms:

s2Y+4Y=1s^2 Y + 4Y = 1

Y=1s2+4Y = \frac{1}{s^2 + 4}

y(t)=12sin(2t)y(t) = \frac{1}{2}\sin(2t).

This is the impulse response (Green’s function) for the operator D2+4D^2 + 4. \blacksquare

5.13 Worked Example: IVP with Laplace

Problem. Solve y6y+9y=6e3ty'' - 6y' + 9y = 6e^{3t}, y(0)=0y(0) = 0, y(0)=0y'(0) = 0.

Solution

Solution. Taking Laplace transforms:

s2Y6sY+9Y=6s3s^2 Y - 6sY + 9Y = \frac{6}{s - 3}

(s3)2Y=6s3(s - 3)^2 Y = \frac{6}{s - 3}

Y=6(s3)3Y = \frac{6}{(s - 3)^3}

Using the table: L{tneat}=n!(sa)n+1\mathcal{L}\{t^n e^{at}\} = \frac{n!}{(s-a)^{n+1}}.

y(t)=6t2e3t2!=3t2e3ty(t) = 6 \cdot \frac{t^2 e^{3t}}{2!} = 3t^2 e^{3t}. \blacksquare

5.14 Common Pitfalls for Laplace Transforms

:::caution Common Pitfall The Laplace transform of y(t)y'(t) is sY(s)y(0)sY(s) - y(0)Not sY(s)sY(s). The Initial conditions are built into the transform. Forgetting them leads to incorrect solutions. :::

:::caution Common Pitfall When using the second shifting theorem, the time-shifted function must Be written as uc(t)f(tc)u_c(t)f(t - c)Not uc(t)f(t)u_c(t)f(t). The function ff must be shifted by the same Amount as the step. :::

5.15 Proof Sketch: Picard Iteration

The Picard-Lindelöf theorem can be proved constructively via Picard iteration. For the IVP y=f(x,y)y' = f(x, y), y(x0)=y0y(x_0) = y_0Define the sequence

ϕ0(x)=y0,ϕn+1(x)=y0+x0xf(t,ϕn(t))dt\phi_0(x) = y_0, \quad \phi_{n+1}(x) = y_0 + \int_{x_0}^x f(t, \phi_n(t))\, dt

If ff and f/y\partial f/\partial y are continuous, one shows by induction that (ϕn)(\phi_n) is uniformly Cauchy on some interval [x0h,x0+h][x_0 - h, x_0 + h]Hence converges uniformly to a function ϕ\phi. Passing to the limit in the integral equation shows ϕ\phi satisfies the ODE. Uniqueness follows From the Gronwall inequality applied to the difference of two solutions.

6. Series Solutions

6.1 Power Series Method

For an ODE y+p(x)y+q(x)y=0y'' + p(x)y' + q(x)y = 0 where pp and qq are analytic near x0x_0Substitute the Power series y=n=0an(xx0)ny = \sum_{n=0}^{\infty} a_n (x - x_0)^n and solve for the coefficients.

6.2 Ordinary and Regular Singular Points

x0x_0 is an ordinary point if pp and qq are analytic at x0x_0. It is a regular singular Point if (xx0)p(x)(x - x_0)p(x) and (xx0)2q(x)(x - x_0)^2 q(x) are analytic at x0x_0.

6.3 Frobenius Method

At a regular singular point x0=0x_0 = 0Substitute y=n=0anxn+ry = \sum_{n=0}^{\infty} a_n x^{n + r}. The indicial equation determines the possible values of rr.

Theorem 6.1. If the roots r1r2r_1 \geq r_2 of the indicial equation differ by a non-integer, there Are two linearly independent solutions of the form xr1anxnx^{r_1}\sum a_n x^n and xr2bnxnx^{r_2}\sum b_n x^n.

6.4 Bessel’s Equation

Bessel’s equation of order ν\nu:

x2y+xy+(x2ν2)y=0x^2 y'' + xy' + (x^2 - \nu^2)y = 0

For νZ\nu \notin \mathbb{Z}The solutions are Jν(x)J_\nu(x) and Jν(x)J_{-\nu}(x) (Bessel functions of the First kind). For ν=nN\nu = n \in \mathbb{N}The second solution is the Weber function Yn(x)Y_n(x).

6.4b Worked Example: Higher-Order ODE

Problem. Solve y6y+11y6y=0y''' - 6y'' + 11y' - 6y = 0.

Solution

Solution. Characteristic equation: r36r2+11r6=0r^3 - 6r^2 + 11r - 6 = 0.

Trying r=1r = 1: 16+116=01 - 6 + 11 - 6 = 0. Factor: (r1)(r25r+6)=(r1)(r2)(r3)=0(r - 1)(r^2 - 5r + 6) = (r - 1)(r - 2)(r - 3) = 0.

Roots: r=1,2,3r = 1, 2, 3 (three distinct real roots).

y=c1ex+c2e2x+c3e3xy = c_1 e^x + c_2 e^{2x} + c_3 e^{3x}. \blacksquare

### 6.5 Worked Example: Power Series Method

Problem. Solve yxy=0y'' - xy = 0 (Airy’s equation) using power series about x0=0x_0 = 0.

Solution

Solution. Since p(x)=0p(x) = 0 and q(x)=xq(x) = -x are both analytic everywhere, x0=0x_0 = 0 is an ordinary Point. Substitute y=n=0anxny = \sum_{n=0}^{\infty} a_n x^n:

y=n=1nanxn1y' = \sum_{n=1}^{\infty} na_n x^{n-1}, y=n=2n(n1)anxn2y'' = \sum_{n=2}^{\infty} n(n-1)a_n x^{n-2}.

yxy=n=2n(n1)anxn2n=0anxn+1=0y'' - xy = \sum_{n=2}^{\infty} n(n-1)a_n x^{n-2} - \sum_{n=0}^{\infty} a_n x^{n+1} = 0.

Shift indices: first sum m=0(m+2)(m+1)am+2xm\sum_{m=0}^{\infty} (m+2)(m+1)a_{m+2} x^mSecond sum m=1am1xm\sum_{m=1}^{\infty} a_{m-1} x^m.

For m=0m = 0: 21a2=0    a2=02 \cdot 1 \cdot a_2 = 0 \implies a_2 = 0.

For m1m \geq 1: (m+2)(m+1)am+2am1=0    am+2=am1(m+2)(m+1)(m+2)(m+1)a_{m+2} - a_{m-1} = 0 \implies a_{m+2} = \frac{a_{m-1}}{(m+2)(m+1)}.

This gives: a3=a06a_3 = \frac{a_0}{6}, a4=a112a_4 = \frac{a_1}{12}, a5=a220=0a_5 = \frac{a_2}{20} = 0 a6=a330=a0180a_6 = \frac{a_3}{30} = \frac{a_0}{180}Etc.

Since a2=0a_2 = 0All a3k+2=0a_{3k+2} = 0.

y(x)=a0(1+x36+x6180+)+a1(x+x412+x7504+)y(x) = a_0\left(1 + \frac{x^3}{6} + \frac{x^6}{180} + \cdots\right) + a_1\left(x + \frac{x^4}{12} + \frac{x^7}{504} + \cdots\right).

These are the Airy functions Ai(x)\mathrm{Ai}(x) and Bi(x)\mathrm{Bi}(x) (up to normalization). \blacksquare

6.6 Worked Example: Frobenius Method

Problem. Solve 2xy+y+xy=02xy'' + y' + xy = 0 near x=0x = 0 using the Frobenius method.

Solution

Solution. Rewrite in standard form: y+12xy+12y=0y'' + \frac{1}{2x}y' + \frac{1}{2}y = 0.

x=0x = 0 is a regular singular point since xp(x)=1/2xp(x) = 1/2 and x2q(x)=x2/2x^2 q(x) = x^2/2 are analytic at 00.

Substitute y=n=0anxn+ry = \sum_{n=0}^{\infty} a_n x^{n+r}, a00a_0 \neq 0:

y=n=0(n+r)anxn+r1y' = \sum_{n=0}^{\infty} (n+r)a_n x^{n+r-1}

y=n=0(n+r)(n+r1)anxn+r2y'' = \sum_{n=0}^{\infty} (n+r)(n+r-1)a_n x^{n+r-2}

Substituting into 2xy+y+xy=02xy'' + y' + xy = 0:

n=02(n+r)(n+r1)anxn+r1+n=0(n+r)anxn+r1+n=0anxn+r+1=0\sum_{n=0}^{\infty} 2(n+r)(n+r-1)a_n x^{n+r-1} + \sum_{n=0}^{\infty} (n+r)a_n x^{n+r-1} + \sum_{n=0}^{\infty} a_n x^{n+r+1} = 0

For n=0n = 0: [2r(r1)+r]a0=0[2r(r-1) + r]a_0 = 0. Since a00a_0 \neq 0: r(2r2+1)=0    r(2r1)=0r(2r - 2 + 1) = 0 \implies r(2r - 1) = 0.

Indicial equation: r=0r = 0 or r=1/2r = 1/2.

For general n1n \geq 1: [2(n+r)(n+r1)+(n+r)]an+an2=0[2(n+r)(n+r-1) + (n+r)]a_n + a_{n-2} = 0

(n+r)(2n+2r1)an=an2(n+r)(2n + 2r - 1)a_n = -a_{n-2}

an=an2(n+r)(2n+2r1)a_n = -\frac{a_{n-2}}{(n+r)(2n + 2r - 1)}

For r=0r = 0: an=an2n(2n1)a_n = -\frac{a_{n-2}}{n(2n-1)}. Odd coefficients vanish (a1=0a_1 = 0). Even: a2=a06a_2 = -\frac{a_0}{6} a4=a0120a_4 = \frac{a_0}{120}Etc.

For r=1/2r = 1/2: an=an2(n+1/2)(2n)=an2n(2n+1)a_n = -\frac{a_{n-2}}{(n+1/2)(2n)} = -\frac{a_{n-2}}{n(2n+1)}.

y=C1k=0a2k(0)x2k+C2x1/2k=0a2k(1/2)x2ky = C_1 \sum_{k=0}^{\infty} a_{2k}^{(0)} x^{2k} + C_2 x^{1/2} \sum_{k=0}^{\infty} a_{2k}^{(1/2)} x^{2k}. \blacksquare

6.7 Worked Example: Bessel Functions

Problem. Find the first three nonzero terms of J0(x)J_0(x)The Bessel function of the first kind Of order zero, which satisfies x2y+xy+x2y=0x^2 y'' + xy' + x^2 y = 0.

Solution

Solution. Here ν=0\nu = 0. The indicial equation gives r2=0r^2 = 0 (repeated root r=0r = 0).

Substituting y=n=0anx2ny = \sum_{n=0}^{\infty} a_n x^{2n} (we can show only even powers appear):

y=n=12nanx2n1y' = \sum_{n=1}^{\infty} 2n a_n x^{2n-1}, y=n=12n(2n1)anx2n2y'' = \sum_{n=1}^{\infty} 2n(2n-1) a_n x^{2n-2}.

x2y+xy+x2y=n=12n(2n1)anx2n+n=12nanx2n+n=0anx2n+2=0x^2 y'' + xy' + x^2 y = \sum_{n=1}^{\infty} 2n(2n-1)a_n x^{2n} + \sum_{n=1}^{\infty} 2n a_n x^{2n} + \sum_{n=0}^{\infty} a_n x^{2n+2} = 0.

For n=0n = 0: a0a_0 is free.

For the recurrence: 4n2an+an1=0    an=an14n24n^2 a_n + a_{n-1} = 0 \implies a_n = -\frac{a_{n-1}}{4n^2} for n1n \geq 1.

a1=a04a_1 = -\frac{a_0}{4}, a2=a064a_2 = \frac{a_0}{64}, a3=a02304a_3 = -\frac{a_0}{2304}.

Setting a0=1a_0 = 1: J0(x)=1x24+x464x62304+J_0(x) = 1 - \frac{x^2}{4} + \frac{x^4}{64} - \frac{x^6}{2304} + \cdots. \blacksquare

## 7. Fourier Series

7.1 Definition

A Fourier series of a 2π2\pi-periodic function ff is

f(x)a02+n=1(ancos(nx)+bnsin(nx))f(x) \sim \frac{a_0}{2} + \sum_{n=1}^{\infty} \left(a_n \cos(nx) + b_n \sin(nx)\right)

Where

an=1πππf(x)cos(nx)dx,bn=1πππf(x)sin(nx)dxa_n = \frac{1}{\pi}\int_{-\pi}^{\pi} f(x)\cos(nx)\, dx, \quad b_n = \frac{1}{\pi}\int_{-\pi}^{\pi} f(x)\sin(nx)\, dx

7.2 Derivation of Fourier Coefficients

The Fourier coefficients are derived using the orthogonality relations on [π,π][-\pi, \pi]:

ππcos(mx)cos(nx)dx={πm=n02πm=n=00mn\int_{-\pi}^{\pi} \cos(mx)\cos(nx)\, dx = \begin{cases} \pi & m = n \neq 0 \\ 2\pi & m = n = 0 \\ 0 & m \neq n \end{cases}

ππsin(mx)sin(nx)dx={πm=n00mn\int_{-\pi}^{\pi} \sin(mx)\sin(nx)\, dx = \begin{cases} \pi & m = n \neq 0 \\ 0 & m \neq n \end{cases}

ππcos(mx)sin(nx)dx=0for  all  m,n\int_{-\pi}^{\pi} \cos(mx)\sin(nx)\, dx = 0 \quad \mathrm{for}\; all\; m, n

To find ana_nMultiply both sides of the Fourier expansion by cos(nx)\cos(nx) and integrate over [π,π][-\pi, \pi]. By orthogonality, all terms vanish except the cos(nx)\cos(nx) term, yielding anπ=ππf(x)cos(nx)dxa_n \pi = \int_{-\pi}^{\pi} f(x)\cos(nx)\, dx. Similarly for bnb_n.

7.3 Convergence

Theorem 7.1 (Dirichlet’s Theorem). If ff is 2π2\pi-periodic and piecewise smooth, its Fourier Series converges to:

  • f(x)f(x) at points where ff is continuous.
  • f(x+)+f(x)2\frac{f(x^+) + f(x^-)}{2} at jump discontinuities.

7.4 Parseval’s Identity

1πππf(x)2dx=a022+n=1(an2+bn2)\frac{1}{\pi}\int_{-\pi}^{\pi} |f(x)|^2\, dx = \frac{a_0^2}{2} + \sum_{n=1}^{\infty}(a_n^2 + b_n^2)

Intuition. Parseval’s identity is the infinite-dimensional analogue of the Pythagorean theorem: The “energy” of ff (its L2L^2 norm squared) equals the sum of the energies of its Fourier Components.

7.5 Sine and Cosine Series

For functions defined on [0,L][0, L]:

  • Cosine series (even extension): an=2L0Lf(x)cosnπxLdxa_n = \frac{2}{L}\int_0^L f(x)\cos\frac{n\pi x}{L}\, dx bn=0b_n = 0.
  • Sine series (odd extension): an=0a_n = 0 bn=2L0Lf(x)sinnπxLdxb_n = \frac{2}{L}\int_0^L f(x)\sin\frac{n\pi x}{L}\, dx.

7.6 Worked Example: Fourier Sine Series

Problem. Find the Fourier series of f(x)=xf(x) = x on (π,π)(-\pi, \pi)Extended 2π2\pi-periodically.

Solution. ff is odd, so an=0a_n = 0 for all nn.

bn=1πππxsin(nx)dx=2π0πxsin(nx)dxb_n = \frac{1}{\pi}\int_{-\pi}^{\pi} x\sin(nx)\, dx = \frac{2}{\pi}\int_0^{\pi} x\sin(nx)\, dx.

Integration by parts: u=xu = x, dv=sin(nx)dxdv = \sin(nx)\, dx:

bn=2π[xcos(nx)n0π+0πcos(nx)ndx]=2π[πcos(nπ)n+0]=2cos(nπ)n=2(1)n+1nb_n = \frac{2}{\pi}\left[-\frac{x\cos(nx)}{n}\Big|_0^{\pi} + \int_0^{\pi} \frac{\cos(nx)}{n}\, dx\right] = \frac{2}{\pi}\left[-\frac{\pi\cos(n\pi)}{n} + 0\right] = \frac{-2\cos(n\pi)}{n} = \frac{2(-1)^{n+1}}{n}.

x2n=1(1)n+1nsin(nx)x \sim 2\sum_{n=1}^{\infty} \frac{(-1)^{n+1}}{n}\sin(nx). \blacksquare

7.7 Worked Example: Fourier Cosine Series

Problem. Find the Fourier cosine series of f(x)=x2f(x) = x^2 on [0,π][0, \pi].

Solution

Solution. Extend ff as an even function on [π,π][-\pi, \pi]. Then bn=0b_n = 0 for all nn.

a0=2π0πx2dx=2ππ33=2π23a_0 = \frac{2}{\pi}\int_0^{\pi} x^2\, dx = \frac{2}{\pi} \cdot \frac{\pi^3}{3} = \frac{2\pi^2}{3}.

For n1n \geq 1: an=2π0πx2cos(nx)dxa_n = \frac{2}{\pi}\int_0^{\pi} x^2\cos(nx)\, dx.

Integrating by parts twice:

u=x2u = x^2, dv=cos(nx)dxdv = \cos(nx)\, dx: du=2xdxdu = 2x\, dx, v=sin(nx)/nv = \sin(nx)/n.

an=2π[x2sin(nx)n0π0π2xsin(nx)ndx]=4nπ0πxsin(nx)dxa_n = \frac{2}{\pi}\left[\frac{x^2\sin(nx)}{n}\Big|_0^{\pi} - \int_0^{\pi} \frac{2x\sin(nx)}{n}\, dx\right] = -\frac{4}{n\pi}\int_0^{\pi} x\sin(nx)\, dx

=4nπ[xcos(nx)n0π+0πcos(nx)ndx]= -\frac{4}{n\pi}\left[-\frac{x\cos(nx)}{n}\Big|_0^{\pi} + \int_0^{\pi} \frac{\cos(nx)}{n}\, dx\right]

=4nπ[πcos(nπ)n+0]=4cos(nπ)n2=4(1)nn2= -\frac{4}{n\pi}\left[-\frac{\pi\cos(n\pi)}{n} + 0\right] = \frac{4\cos(n\pi)}{n^2} = \frac{4(-1)^n}{n^2}.

x2π23+4n=1(1)nn2cos(nx)x^2 \sim \frac{\pi^2}{3} + 4\sum_{n=1}^{\infty} \frac{(-1)^n}{n^2}\cos(nx).

Setting x=0x = 0: 0=π23+4n=1(1)nn20 = \frac{\pi^2}{3} + 4\sum_{n=1}^{\infty} \frac{(-1)^n}{n^2}Giving the famous identity n=1(1)n+1n2=π212\sum_{n=1}^{\infty} \frac{(-1)^{n+1}}{n^2} = \frac{\pi^2}{12}. \blacksquare

7.8 Complex Fourier Series

Using Euler’s formula, the Fourier series can be written in complex form:

f(x)n=cneinxf(x) \sim \sum_{n=-\infty}^{\infty} c_n e^{inx}

Where cn=12πππf(x)einxdxc_n = \frac{1}{2\pi}\int_{-\pi}^{\pi} f(x)e^{-inx}\, dx.

The relationship with the real coefficients is c0=a0/2c_0 = a_0/2, cn=(anibn)/2c_n = (a_n - ib_n)/2 for n>0n > 0 And cn=cnc_{-n} = \overline{c_n} when ff is real-valued.

7.9 Worked Example: Parseval’s Identity

Problem. Using the Fourier series of f(x)=xf(x) = x on (π,π)(-\pi, \pi)Verify Parseval’s identity And deduce n=11n2=π26\sum_{n=1}^{\infty} \frac{1}{n^2} = \frac{\pi^2}{6}.

Solution

Solution. From Section 7.6: a0=0a_0 = 0, an=0a_n = 0, bn=2(1)n+1nb_n = \frac{2(-1)^{n+1}}{n}.

Parseval: 1πππx2dx=n=1bn2=n=14n2\frac{1}{\pi}\int_{-\pi}^{\pi} x^2\, dx = \sum_{n=1}^{\infty} b_n^2 = \sum_{n=1}^{\infty} \frac{4}{n^2}.

1π2π33=4n=11n2\frac{1}{\pi} \cdot \frac{2\pi^3}{3} = 4\sum_{n=1}^{\infty} \frac{1}{n^2}.

2π23=4n=11n2\frac{2\pi^2}{3} = 4\sum_{n=1}^{\infty} \frac{1}{n^2}.

n=11n2=π26\sum_{n=1}^{\infty} \frac{1}{n^2} = \frac{\pi^2}{6}. \blacksquare

## 8. Introduction to Partial Differential Equations

8.1 Classification of Second-Order PDEs

The general second-order linear PDE in two variables is

Auxx+Buxy+Cuyy+Dux+Euy+Fu=GA u_{xx} + B u_{xy} + C u_{yy} + D u_x + E u_y + F u = G

  • Elliptic (B24AC<0B^2 - 4AC \lt 0): e.g., Laplace’s equation uxx+uyy=0u_{xx} + u_{yy} = 0.
  • Parabolic (B24AC=0B^2 - 4AC = 0): e.g., the heat equation ut=α2uxxu_t = \alpha^2 u_{xx}.
  • Hyperbolic (B24AC>0B^2 - 4AC > 0): e.g., the wave equation utt=c2uxxu_{tt} = c^2 u_{xx}.

8.2 The Heat Equation

ut=α2uxx,0<x<L,t>0u_t = \alpha^2 u_{xx}, \quad 0 \lt x \lt L, \quad t > 0

With boundary conditions u(0,t)=u(L,t)=0u(0, t) = u(L, t) = 0 and initial condition u(x,0)=f(x)u(x, 0) = f(x).

8.3 Derivation of the Heat Equation

Consider a thin rod of length LL with uniform cross-section and density ρ\rho. Let u(x,t)u(x, t) be the Temperature at position xx and time tt. By Fourier’s law of heat conduction, the heat flux Through a cross-section is proportional to the negative temperature gradient:

q=κuxq = -\kappa u_x

Where κ\kappa is the thermal conductivity. Conservation of energy on [x,x+Δx][x, x + \Delta x]:

ρcutΔx=q(x)q(x+Δx)=κux(x)+κux(x+Δx)\rho c \frac{\partial u}{\partial t} \Delta x = q(x) - q(x + \Delta x) = -\kappa u_x(x) + \kappa u_x(x + \Delta x)

Dividing by Δx\Delta x and taking Δx0\Delta x \to 0:

ρcut=κuxx    ut=κρcuxx=α2uxx\rho c \, u_t = \kappa u_{xx} \implies u_t = \frac{\kappa}{\rho c} u_{xx} = \alpha^2 u_{xx}

Where α2=κ/(ρc)\alpha^2 = \kappa/(\rho c) is the thermal diffusivity.

8.4 Solving the Heat Equation by Separation of Variables

Assume u(x,t)=X(x)T(t)u(x, t) = X(x)T(t). Substituting:

XT=α2XT    Tα2T=XX=λX T' = \alpha^2 X'' T \implies \frac{T'}{\alpha^2 T} = \frac{X''}{X} = -\lambda

This gives two ODEs:

X+λX=0,X(0)=X(L)=0X'' + \lambda X = 0, \quad X(0) = X(L) = 0 T+α2λT=0T' + \alpha^2 \lambda T = 0

The boundary value problem for XX has solutions only for λn=(nπ/L)2\lambda_n = (n\pi/L)^2 n=1,2,3,n = 1, 2, 3, \ldotsWith Xn(x)=sin(nπx/L)X_n(x) = \sin(n\pi x/L).

The corresponding Tn(t)=eα2(nπ/L)2tT_n(t) = e^{-\alpha^2 (n\pi/L)^2 t}.

By superposition:

u(x,t)=n=1bnsinnπxLeα2(nπ/L)2tu(x, t) = \sum_{n=1}^{\infty} b_n \sin\frac{n\pi x}{L} e^{-\alpha^2 (n\pi/L)^2 t}

Where bn=2L0Lf(x)sinnπxLdxb_n = \frac{2}{L}\int_0^L f(x)\sin\frac{n\pi x}{L}\, dx (the sine series coefficients of ff).

8.5 Worked Example: Heat Equation

Problem. Solve ut=uxxu_t = u_{xx} for 0<x<π0 \lt x \lt \pi, t>0t > 0With u(0,t)=u(π,t)=0u(0, t) = u(\pi, t) = 0 And u(x,0)=sin(2x)+3sin(5x)u(x, 0) = \sin(2x) + 3\sin(5x).

Solution. Here α=1\alpha = 1 and L=πL = \pi. The initial condition is already a sine series.

λn=n2\lambda_n = n^2, Xn=sin(nx)X_n = \sin(nx), Tn=en2tT_n = e^{-n^2 t}.

u(x,t)=e4tsin(2x)+3e25tsin(5x)u(x, t) = e^{-4t}\sin(2x) + 3e^{-25t}\sin(5x). \blacksquare

8.6 The Wave Equation

utt=c2uxx,0<x<L,t>0u_{tt} = c^2 u_{xx}, \quad 0 \lt x \lt L, \quad t > 0

With boundary conditions u(0,t)=u(L,t)=0u(0, t) = u(L, t) = 0And initial conditions u(x,0)=f(x)u(x, 0) = f(x) ut(x,0)=g(x)u_t(x, 0) = g(x).

8.7 Derivation of the Wave Equation

Consider a string of length LL under tension TT. Let u(x,t)u(x, t) be the vertical displacement. For A small segment [x,x+Δx][x, x + \Delta x]Newton’s second law in the vertical direction gives:

ρΔxutt=Tsinθ(x+Δx)Tsinθ(x)\rho \Delta x \, u_{tt} = T\sin\theta(x + \Delta x) - T\sin\theta(x)

For small displacements, sinθtanθ=ux\sin\theta \approx \tan\theta = u_xSo:

ρutt=Tux(x+Δx)ux(x)ΔxΔx0Tuxx\rho \, u_{tt} = T \frac{u_x(x + \Delta x) - u_x(x)}{\Delta x} \xrightarrow{\Delta x \to 0} T u_{xx}

utt=Tρuxx=c2uxx,c=T/ρu_{tt} = \frac{T}{\rho} u_{xx} = c^2 u_{xx}, \quad c = \sqrt{T/\rho}

8.8 Solving the Wave Equation

Separation of variables u(x,t)=X(x)T(t)u(x, t) = X(x)T(t) gives:

X+λX=0,T+c2λT=0X'' + \lambda X = 0, \quad T'' + c^2 \lambda T = 0

With λn=(nπ/L)2\lambda_n = (n\pi/L)^2:

Xn(x)=sinnπxL,Tn(t)=ancoscnπtL+bnsincnπtLX_n(x) = \sin\frac{n\pi x}{L}, \quad T_n(t) = a_n \cos\frac{cn\pi t}{L} + b_n \sin\frac{cn\pi t}{L}

u(x,t)=n=1sinnπxL(ancoscnπtL+bnsincnπtL)u(x, t) = \sum_{n=1}^{\infty} \sin\frac{n\pi x}{L}\left(a_n \cos\frac{cn\pi t}{L} + b_n \sin\frac{cn\pi t}{L}\right)

Where an=2L0Lf(x)sinnπxLdxa_n = \frac{2}{L}\int_0^L f(x)\sin\frac{n\pi x}{L}\, dx and bn=2cnπ0Lg(x)sinnπxLdxb_n = \frac{2}{cn\pi}\int_0^L g(x)\sin\frac{n\pi x}{L}\, dx.

8.9 D’Alembert’s Solution

For the wave equation on <x<-\infty \lt x \lt \infty:

u(x,t)=f(x+ct)+f(xct)2+12cxctx+ctg(s)dsu(x, t) = \frac{f(x + ct) + f(x - ct)}{2} + \frac{1}{2c}\int_{x - ct}^{x + ct} g(s)\, ds

This represents the solution as a superposition of right-moving and left-moving waves.

8.10 Laplace’s Equation

uxx+uyy=0u_{xx} + u_{yy} = 0

On a domain ΩR2\Omega \subseteq \mathbb{R}^2With boundary conditions on Ω\partial\Omega.

Theorem 8.1 (Maximum Principle). A harmonic function uu (satisfying Laplace’s equation) on a Bounded domain attains its maximum and minimum on the boundary.

Theorem 8.2 (Uniqueness). The Dirichlet problem for Laplace’s equation has at most one solution.

Proof. If u1u_1 and u2u_2 are two solutions with the same boundary data, then v=u1u2v = u_1 - u_2 is Harmonic with v=0v = 0 on Ω\partial\Omega. By the maximum principle, v0v \equiv 0. \blacksquare

8.11 Worked Example: Wave Equation

Problem. A string of length π\pi with fixed ends is plucked: u(x,0)=x(πx)u(x, 0) = x(\pi - x) ut(x,0)=0u_t(x, 0) = 0. Find u(x,t)u(x, t).

Solution. With c=1c = 1 and L=πL = \pi: an=2π0πx(πx)sin(nx)dxa_n = \frac{2}{\pi}\int_0^{\pi} x(\pi - x)\sin(nx)\, dx bn=0b_n = 0 (since g=0g = 0).

Integrating by parts twice:

0πx(πx)sin(nx)dx=[x(πx)cos(nx)n]0π+1n0π(π2x)cos(nx)dx\int_0^{\pi} x(\pi - x)\sin(nx)\, dx = \left[-\frac{x(\pi - x)\cos(nx)}{n}\right]_0^{\pi} + \frac{1}{n}\int_0^{\pi}(\pi - 2x)\cos(nx)\, dx

=0+1n[(π2x)sin(nx)n]0π+2n20πsin(nx)dx= 0 + \frac{1}{n}\left[\frac{(\pi - 2x)\sin(nx)}{n}\right]_0^{\pi} + \frac{2}{n^2}\int_0^{\pi}\sin(nx)\, dx

=0+2n2[cos(nx)n]0π=2n3(1(1)n)= 0 + \frac{2}{n^2}\left[-\frac{\cos(nx)}{n}\right]_0^{\pi} = \frac{2}{n^3}(1 - (-1)^n)

For even nn: an=0a_n = 0. For odd n=2k+1n = 2k + 1: an=2π4n3=8πn3a_n = \frac{2}{\pi} \cdot \frac{4}{n^3} = \frac{8}{\pi n^3}.

u(x,t)=8πk=0sin((2k+1)x)(2k+1)3cos((2k+1)t)u(x, t) = \frac{8}{\pi}\sum_{k=0}^{\infty} \frac{\sin((2k+1)x)}{(2k+1)^3}\cos((2k+1)t). \blacksquare

8.12 Worked Example: Laplace’s Equation on a Rectangle

Problem. Solve uxx+uyy=0u_{xx} + u_{yy} = 0 on 0<x<π0 \lt x \lt \pi, 0<y<10 \lt y \lt 1 with u(0,y)=u(π,y)=u(x,1)=0u(0, y) = u(\pi, y) = u(x, 1) = 0 and u(x,0)=f(x)=x(πx)u(x, 0) = f(x) = x(\pi - x).

Solution

Solution. Separate variables: u(x,y)=X(x)Y(y)u(x, y) = X(x)Y(y).

X/X=Y/Y=λX''/X = -Y''/Y = -\lambda.

X+λX=0X'' + \lambda X = 0, X(0)=X(π)=0X(0) = X(\pi) = 0: λn=n2\lambda_n = n^2, Xn=sin(nx)X_n = \sin(nx).

Yn2Y=0Y'' - n^2 Y = 0, Y(1)=0Y(1) = 0: Yn=sinh(n(1y))Y_n = \sinh(n(1 - y)).

u(x,y)=n=1bnsin(nx)sinh(n(1y))u(x, y) = \sum_{n=1}^{\infty} b_n \sin(nx)\sinh(n(1-y)).

bn=2πsinhn0πx(πx)sin(nx)dx=2πsinhn2(1(1)n)n3b_n = \frac{2}{\pi \sinh n}\int_0^{\pi} x(\pi - x)\sin(nx)\, dx = \frac{2}{\pi \sinh n} \cdot \frac{2(1 - (-1)^n)}{n^3}.

For odd n=2k+1n = 2k + 1: bn=8πn3sinhnb_n = \frac{8}{\pi n^3 \sinh n}.

u(x,y)=8πk=0sin((2k+1)x)sinh((2k+1)(1y))(2k+1)3sinh(2k+1)u(x, y) = \frac{8}{\pi}\sum_{k=0}^{\infty} \frac{\sin((2k+1)x)\sinh((2k+1)(1-y))}{(2k+1)^3 \sinh(2k+1)}. \blacksquare

8.13 Sturm-Liouville Theory (Brief)

A Sturm-Liouville problem consists of the ODE

(p(x)y)+[λw(x)q(x)]y=0(p(x)y')' + [\lambda w(x) - q(x)]y = 0

On [a,b][a, b] with homogeneous boundary conditions, where p,w>0p, w > 0 and p,p,q,wp, p', q, w are continuous.

Key properties:

  1. The eigenvalues are real and form an infinite increasing sequence λ1<λ2<\lambda_1 \lt \lambda_2 \lt \cdots \to \infty.
  2. Eigenfunctions corresponding to distinct eigenvalues are orthogonal with respect to the weight w(x)w(x): abym(x)yn(x)w(x)dx=0\int_a^b y_m(x) y_n(x) w(x)\, dx = 0 for mnm \neq n.
  3. The eigenfunctions form a complete set in the weighted L2L^2 space.

Remark. The boundary value problems encountered in the heat and wave equations (X+λX=0X'' + \lambda X = 0 with X(0)=X(L)=0X(0) = X(L) = 0) are special cases of Sturm-Liouville problems With p=1p = 1, q=0q = 0, w=1w = 1.

8.14 Neumann Boundary Conditions

When the boundary specifies the derivative (heat flux) rather than the value, we have Neumann Conditions. For the heat equation:

ux(0,t)=0,ux(L,t)=0u_x(0, t) = 0, \quad u_x(L, t) = 0

(insulated ends). The separation of variables gives X(0)=X(L)=0X'(0) = X'(L) = 0Yielding eigenvalues λ0=0\lambda_0 = 0 with X0=1X_0 = 1And λn=(nπ/L)2\lambda_n = (n\pi/L)^2 for n1n \geq 1 with Xn=cos(nπx/L)X_n = \cos(n\pi x/L).

The solution is

u(x,t)=a02+n=1ancosnπxLeα2(nπ/L)2tu(x, t) = \frac{a_0}{2} + \sum_{n=1}^{\infty} a_n \cos\frac{n\pi x}{L} e^{-\alpha^2 (n\pi/L)^2 t}

Where an=2L0Lf(x)cosnπxLdxa_n = \frac{2}{L}\int_0^L f(x)\cos\frac{n\pi x}{L}\, dx.

Remark. As tt \to \inftyAll exponential terms decay, and u(x,t)a0/2u(x, t) \to a_0/2The average Value of the initial temperature. Physically, an insulated rod reaches a uniform steady-state Temperature.

8.15 Worked Example: Heat Equation with Non-Trivial Initial Data

Problem. Solve ut=uxxu_t = u_{xx} for 0<x<π0 \lt x \lt \pi, t>0t > 0With u(0,t)=u(π,t)=0u(0, t) = u(\pi, t) = 0 And u(x,0)=x(πx)u(x, 0) = x(\pi - x).

Solution

Solution. The sine series of f(x)=x(πx)f(x) = x(\pi - x) on [0,π][0, \pi] has coefficients

bn=2π0πx(πx)sin(nx)dx=4(1(1)n)πn3b_n = \frac{2}{\pi}\int_0^{\pi} x(\pi - x)\sin(nx)\, dx = \frac{4(1 - (-1)^n)}{\pi n^3}.

(Computed in Section 8.11.)

For even nn: bn=0b_n = 0. For odd n=2k+1n = 2k + 1: bn=8πn3b_n = \frac{8}{\pi n^3}.

u(x,t)=8πk=0sin((2k+1)x)(2k+1)3e(2k+1)2tu(x, t) = \frac{8}{\pi}\sum_{k=0}^{\infty} \frac{\sin((2k+1)x)}{(2k+1)^3} e^{-(2k+1)^2 t}. \blacksquare

8.16 Worked Example: D’Alembert’s Solution

Problem. Solve utt=4uxxu_{tt} = 4u_{xx} for <x<-\infty \lt x \lt \infty with u(x,0)=ex2u(x, 0) = e^{-x^2} and ut(x,0)=0u_t(x, 0) = 0.

Solution

Solution. Here c=2c = 2. By D’Alembert’s formula with g=0g = 0:

u(x,t)=f(x+2t)+f(x2t)2=e(x+2t)2+e(x2t)22u(x, t) = \frac{f(x + 2t) + f(x - 2t)}{2} = \frac{e^{-(x+2t)^2} + e^{-(x-2t)^2}}{2}.

This represents two Gaussian pulses traveling in opposite directions at speed 2. \blacksquare

## 9. Stability and Phase Plane Analysis

9.1 Autonomous Systems

For x=f(x)\mathbf{x}' = \mathbf{f}(\mathbf{x})A critical point x\mathbf{x}^* satisfies f(x)=0\mathbf{f}(\mathbf{x}^*) = \mathbf{0}.

9.2 Linearization and Stability

Let A=Jf(x)A = J\mathbf{f}(\mathbf{x}^*) be the Jacobian at the critical point. The eigenvalues of AA Determine the local stability:

Eigenvalues of AATypeStability
Both real, negativeStable nodeAsymptotically stable
Both real, positiveUnstable nodeUnstable
Real, opposite signsSaddle pointUnstable
Complex, Re(λ)<0\mathrm{Re}(\lambda) \lt 0Stable spiralAsymptotically stable
Complex, Re(λ)>0\mathrm{Re}(\lambda) > 0Unstable spiralUnstable
Purely imaginaryCenter(Marginally) stable

9.3 Lyapunov Stability

Definition. A critical point x\mathbf{x}^* is stable if for every ε>0\varepsilon > 0There Exists δ>0\delta > 0 such that x(0)x<δ\|\mathbf{x}(0) - \mathbf{x}^*\| \lt \delta implies x(t)x<ε\|\mathbf{x}(t) - \mathbf{x}^*\| \lt \varepsilon for all t>0t > 0.

It is asymptotically stable if it is stable and x(t)x\mathbf{x}(t) \to \mathbf{x}^* as tt \to \infty.

Theorem 9.1 (Lyapunov). If there exists a continuously differentiable function VV (a Lyapunov Function) such that V(x)=0V(\mathbf{x}^*) = 0, V(x)>0V(\mathbf{x}) > 0 for xx\mathbf{x} \neq \mathbf{x}^* And V˙0\dot{V} \leq 0 in a neighbourhood of x\mathbf{x}^*Then x\mathbf{x}^* is stable. If V˙<0\dot{V} \lt 0 for xx\mathbf{x} \neq \mathbf{x}^*Then x\mathbf{x}^* is asymptotically stable.

9.4 Worked Example: Linearization

Problem. Find and classify the critical points of x=xyx' = x - y, y=x2+y21y' = x^2 + y^2 - 1.

Solution

Solution. Set x=0x' = 0 and y=0y' = 0:

xy=0    y=xx - y = 0 \implies y = x

x2+x21=0    2x2=1    x=±1/2x^2 + x^2 - 1 = 0 \implies 2x^2 = 1 \implies x = \pm 1/\sqrt{2}

Critical points: (1/2,1/2)(1/\sqrt{2}, 1/\sqrt{2}) and (1/2,1/2)(-1/\sqrt{2}, -1/\sqrt{2}).

The Jacobian is J=(112x2y)J = \begin{pmatrix} 1 & -1 \\ 2x & 2y \end{pmatrix}.

At (1/2,1/2)(1/\sqrt{2}, 1/\sqrt{2}): J=(1122)J = \begin{pmatrix} 1 & -1 \\ \sqrt{2} & \sqrt{2} \end{pmatrix}.

tr(J)=1+2>0\mathrm{tr}(J) = 1 + \sqrt{2} > 0, det(J)=2+2=22>0\det(J) = \sqrt{2} + \sqrt{2} = 2\sqrt{2} > 0.

τ24Δ=(1+2)282=3+2282=362<0\tau^2 - 4\Delta = (1 + \sqrt{2})^2 - 8\sqrt{2} = 3 + 2\sqrt{2} - 8\sqrt{2} = 3 - 6\sqrt{2} \lt 0.

Complex eigenvalues with positive real part: unstable spiral.

At (1/2,1/2)(-1/\sqrt{2}, -1/\sqrt{2}): J=(1122)J = \begin{pmatrix} 1 & -1 \\ -\sqrt{2} & -\sqrt{2} \end{pmatrix}.

tr(J)=12<0\mathrm{tr}(J) = 1 - \sqrt{2} \lt 0, det(J)=2+2=0\det(J) = -\sqrt{2} + \sqrt{2} = 0.

Wait, det(J)=(1)(2)(1)(2)=22=22<0\det(J) = (1)(-\sqrt{2}) - (-1)(-\sqrt{2}) = -\sqrt{2} - \sqrt{2} = -2\sqrt{2} \lt 0.

Negative determinant: saddle point (unstable). \blacksquare

9.5 Phase Portraits for 2D Nonlinear Systems

For the nonlinear system x=f(x)\mathbf{x}' = \mathbf{f}(\mathbf{x})The Hartman-Grobman theorem States that near a hyperbolic critical point (one where the Jacobian has no eigenvalues on the Imaginary axis), the nonlinear phase portrait is topologically equivalent to the linearized one.

Procedure for sketching phase portraits:

  1. Find all critical points by solving f(x)=0\mathbf{f}(\mathbf{x}) = \mathbf{0}.
  2. Compute the Jacobian JJ at each critical point.
  3. Classify each critical point using the eigenvalue analysis from Section 4.9.
  4. Sketch the local behaviour near each critical point.
  5. Connect the local pictures using nullclines (x=0x' = 0 and y=0y' = 0 curves).

9.6 Limit Cycles and Poincaré-Bendixson

A limit cycle is an isolated closed periodic orbit. Limit cycles are inherently nonlinear Phenomena --- linear systems cannot have isolated closed orbits.

Theorem 9.2 (Poincaré-Bendixson). If a trajectory of a C1C^1 planar system is confined to a Closed bounded region RR that contains no critical points, then the trajectory approaches a closed Periodic orbit as tt \to \infty.

Remark. The Poincaré-Bendixson theorem is specific to two dimensions. In three or more Dimensions, much more complex behaviour (chaos) is possible.

Example: Van der Pol oscillator. The equation

x+μ(x21)x+x=0x'' + \mu(x^2 - 1)x' + x = 0

With μ>0\mu > 0 has a unique stable limit cycle. This system models electrical circuits with Nonlinear resistance and arises in biology (cardiac rhythms, neuron firing).

9.7 Worked Example: Lotka-Volterra Analysis

Problem. Analyze the stability of the Lotka-Volterra system x=x(2y)x' = x(2 - y), y=y(x1)y' = y(x - 1).

Solution

Solution. Critical points: (0,0)(0, 0) and (1,2)(1, 2).

Jacobian: J=(2yxyx1)J = \begin{pmatrix} 2 - y & -x \\ y & x - 1 \end{pmatrix}.

At (0,0)(0, 0): J=(2001)J = \begin{pmatrix} 2 & 0 \\ 0 & -1 \end{pmatrix}. Eigenvalues 22 and 1-1: saddle point (unstable).

At (1,2)(1, 2): J=(0120)J = \begin{pmatrix} 0 & -1 \\ 2 & 0 \end{pmatrix}. det(J)=2>0\det(J) = 2 > 0, tr(J)=0\mathrm{tr}(J) = 0. Eigenvalues ±i2\pm i\sqrt{2}: center.

Remark. For the linearized system, the center is (marginally) stable. However, for the Nonlinear Lotka-Volterra system, the trajectories are actually closed orbits surrounding (1,2)(1, 2). This can be verified using the first integral H=xlnx+2lnyyH = x - \ln x + 2\ln y - yWhich is constant Along trajectories. \blacksquare

9.8 Competing Species

The competing species model is:

x=x(r1a11xa12y),y=y(r2a21xa22y)x' = x(r_1 - a_{11}x - a_{12}y), \quad y' = y(r_2 - a_{21}x - a_{22}y)

Where ri>0r_i > 0 are growth rates and aij>0a_{ij} > 0 are competition coefficients. The four critical Points are (0,0)(0, 0), (r1/a11,0)(r_1/a_{11}, 0), (0,r2/a22)(0, r_2/a_{22})And the coexistence point (x,y)(x^*, y^*) where both xx' and yy' vanish.

The stability of the coexistence point determines whether both species survive. If a11a22>a12a21a_{11}a_{22} > a_{12}a_{21}Coexistence is stable; otherwise, one species drives the other To extinction (competitive exclusion).

10. Common Pitfalls

:::caution Common Pitfall When using undetermined coefficients, always check whether your guess Overlaps with the homogeneous solution. For y4y=e2xy'' - 4y = e^{2x}Guessing yp=Ae2xy_p = Ae^{2x} fails Because e2xe^{2x} satisfies the homogeneous equation. You must use yp=Axe2xy_p = Axe^{2x} instead. :::

:::caution Common Pitfall The Laplace transform of y(t)y'(t) is sY(s)y(0)sY(s) - y(0)Not sY(s)sY(s). The Initial conditions are built into the transform. Forgetting them leads to incorrect solutions. :::

:::caution Common Pitfall Separation of variables can miss solutions. When dividing by h(y)h(y) to Separate, check whether h(y)=0h(y) = 0 gives any valid solutions. For example, solving y=y2y' = y^2 by Separating gives y=1/(x+C)y = -1/(x + C)But misses the solution y=0y = 0. :::

:::caution Common Pitfall The Fourier series of a function converges to the function only at points Of continuity. At jump discontinuities, it converges to the midpoint of the jump. The Gibbs Phenomenon causes overshoots near jumps that do not vanish as more terms are added. :::

:::caution Common Pitfall When solving PDEs by separation of variables, the boundary conditions Determine the eigenvalues. Using the wrong boundary conditions (e.g., Neumann instead of Dirichlet) Leads to a completely different set of eigenfunctions and eigenvalues. :::

:::caution Common Pitfall Not every first-order ODE can be solved analytically. Equations like y=x2+y2y' = x^2 + y^2 have no closed-form solution in terms of elementary functions. Numerical methods (Euler, Runge-Kutta) may be necessary. :::

:::caution Common Pitfall The linearization of a nonlinear system near a critical point is only Valid for hyperbolic critical points (no eigenvalues on the imaginary axis). If eigenvalues lie on The imaginary axis, the nonlinear system can behave very differently from its linearization. :::

:::caution Common Pitfall When computing the inverse Laplace transform, always check that the Partial fraction decomposition is correct before inverting term-by-term. A common error is Forgetting to include all powers of irreducible quadratic factors. :::

:::caution Common Pitfall For the Euler-Cauchy equation x2y+axy+by=0x^2 y'' + axy' + by = 0Remember that xrx^r with complex r=α±iβr = \alpha \pm i\beta gives solutions involving cos(βlnx)\cos(\beta \ln x) and sin(βlnx)\sin(\beta \ln x)Not cos(βx)\cos(\beta x) and sin(βx)\sin(\beta x). :::

:::caution Common Pitfall The Wronskian W(y1,y2)W(y_1, y_2) being zero at a single point does not Necessarily mean the solutions are linearly dependent. For linear ODEs with continuous coefficients, W0W \equiv 0 everywhere or W0W \neq 0 everywhere. Check Abel’s identity.

9.9 Worked Example: Lyapunov Function

Problem. Show that (0,0)(0, 0) is asymptotically stable for the system x=xy3x' = -x - y^3, y=x3yy' = x^3 - y.

Solution

Solution. Critical point: xy3=0-x - y^3 = 0 and x3y=0x^3 - y = 0 gives x=0,y=0x = 0, y = 0.

Choose the Lyapunov function V(x,y)=x22+y22V(x, y) = \frac{x^2}{2} + \frac{y^2}{2}. V(0,0)=0V(0,0) = 0 and V>0V > 0 for (x,y)(0,0)(x, y) \neq (0, 0).

V˙=Vxx+Vyy=x(xy3)+y(x3y)\dot{V} = \frac{\partial V}{\partial x}x' + \frac{\partial V}{\partial y}y' = x(-x - y^3) + y(x^3 - y)

=x2xy3+x3yy2=x2y2+xy(x2y2)= -x^2 - xy^3 + x^3 y - y^2 = -x^2 - y^2 + xy(x^2 - y^2)

This is not negative definite. Let us try V(x,y)=x44+y44V(x, y) = \frac{x^4}{4} + \frac{y^4}{4}:

V˙=x3(xy3)+y3(x3y)=x4x3y3+x3y3y4=(x4+y4)\dot{V} = x^3(-x - y^3) + y^3(x^3 - y) = -x^4 - x^3 y^3 + x^3 y^3 - y^4 = -(x^4 + y^4).

Since V˙=(x4+y4)<0\dot{V} = -(x^4 + y^4) \lt 0 for (x,y)(0,0)(x, y) \neq (0, 0)The origin is asymptotically Stable by Lyapunov’s theorem. \blacksquare

9.10 Worked Example: Van der Pol Oscillator

Problem. Show that the Van der Pol equation x+μ(x21)x+x=0x'' + \mu(x^2 - 1)x' + x = 0 with μ>0\mu > 0 Has a unique limit cycle.

Solution

_Solution.* Write as a system: x=yx' = y, y=xμ(x21)yy' = -x - \mu(x^2 - 1)y.

The only critical point is (0,0)(0, 0). The Jacobian at the origin is J=(011μ)J = \begin{pmatrix} 0 & 1 \\ -1 & \mu \end{pmatrix}.

tr(J)=μ>0\mathrm{tr}(J) = \mu > 0, det(J)=1>0\det(J) = 1 > 0, τ24Δ=μ24\tau^2 - 4\Delta = \mu^2 - 4.

For 0<μ<20 \lt \mu \lt 2: complex eigenvalues with positive real part (unstable spiral). For μ2\mu \geq 2: two positive real eigenvalues (unstable node).

The origin is always unstable. To show existence of a limit cycle, we use a Liénard’s theorem Or construct a trapping region. Define

L(x)=x3/3xL(x) = x^3/3 - x and write the system as x=yx' = y, y=xμL(x)yy' = -x - \mu L'(x) y.

The function L(x)L(x) has zeros at x=±1x = \pm 1. For μ>0\mu > 0The damping is negative for x<1\lvert x \rvert \lt 1 (energy input) and positive for x>1\lvert x \rvert > 1 (energy Dissipation). This creates a unique stable limit cycle that passes through x=±1x = \pm 1.

A detailed proof requires constructing an annular region and applying the Poincaré-Bendixson Theorem (the inner boundary encloses the unstable origin; the outer boundary is chosen so that Trajectories point inward). \blacksquare

## 11. Problem Set

Problem 1 (Classification)

Classify the ODE y+xy+exy=cosxy'' + xy' + e^x y = \cos x by order, linearity, and homogeneity.

Solution

Solution. Second-order (highest derivative is yy''), linear (yy, yy', yy'' appear linearly With coefficient functions of xx only), nonhomogeneous (cosx0\cos x \neq 0). \blacksquare

If you get this wrong, revise: Section 1.2 (Classification of ODEs).

Problem 2 (Separable Equation)

Solve dydx=xy\frac{dy}{dx} = \frac{x}{y}, y(0)=2y(0) = 2.

Solution

Solution. Separating: ydy=xdxy\, dy = x\, dx. Integrating: y22=x22+C\frac{y^2}{2} = \frac{x^2}{2} + C.

y(0)=2    C=2y(0) = 2 \implies C = 2So y2=x2+4y^2 = x^2 + 4Giving y=x2+4y = \sqrt{x^2 + 4} (positive branch Since y(0)=2>0y(0) = 2 > 0). \blacksquare

If you get this wrong, revise: Section 2.1 (Separable Equations).

Problem 3 (Linear First-Order)

Solve y+yx=x2y' + \frac{y}{x} = x^2 for x>0x > 0, y(1)=1y(1) = 1.

Solution

Solution. P(x)=1/xP(x) = 1/x, Q(x)=x2Q(x) = x^2.

μ(x)=e1/xdx=elnx=x\mu(x) = e^{\int 1/x\, dx} = e^{\ln x} = x.

y=x1(xx2dx+C)=x1(x44+C)=x34+Cxy = x^{-1}\left(\int x \cdot x^2\, dx + C\right) = x^{-1}\left(\frac{x^4}{4} + C\right) = \frac{x^3}{4} + \frac{C}{x}.

y(1)=1/4+C=1    C=3/4y(1) = 1/4 + C = 1 \implies C = 3/4.

y=x34+34xy = \frac{x^3}{4} + \frac{3}{4x}. \blacksquare

If you get this wrong, revise: Section 2.2 (Linear First-Order Equations).

Problem 4 (Exact Equation)

Solve (2x+y)dx+(x+2y)dy=0(2x + y)\, dx + (x + 2y)\, dy = 0.

Solution

Solution. M=2x+yM = 2x + y, N=x+2yN = x + 2y. My=1=NxM_y = 1 = N_x. Exact.

Ψx=2x+y    Ψ=x2+xy+h(y)\Psi_x = 2x + y \implies \Psi = x^2 + xy + h(y).

Ψy=x+h(y)=x+2y    h(y)=2y    h(y)=y2\Psi_y = x + h'(y) = x + 2y \implies h'(y) = 2y \implies h(y) = y^2.

Solution: x2+xy+y2=Cx^2 + xy + y^2 = C. \blacksquare

If you get this wrong, revise: Section 2.4 (Exact Equations).

Problem 5 (Bernoulli Equation)

Solve yy=xy2y' - y = xy^2.

Solution

Solution. This is Bernoulli with n=2n = 2, P(x)=1P(x) = -1, Q(x)=xQ(x) = x.

Substitution v=y1v = y^{-1}: v=y2yv' = -y^{-2}y'So vv=x-v' - v = xI.e., v+v=xv' + v = -x.

Integrating factor: exe^x. (vex)=xex(ve^x)' = -xe^x.

vex=xex+ex+C=ex(1x)+Cve^x = -xe^x + e^x + C = e^x(1 - x) + C.

v=1x+Cexv = 1 - x + Ce^{-x}So y=11x+Cexy = \frac{1}{1 - x + Ce^{-x}}. \blacksquare

If you get this wrong, revise: Section 2.7 (Bernoulli Equations).

Problem 6 (Homogeneous Substitution)

Solve y=x+yxyy' = \frac{x + y}{x - y} using the substitution y=vxy = vx.

Solution

Solution. y=vx    y=v+xvy = vx \implies y' = v + xv'.

x+vxxvx=1+v1v\frac{x + vx}{x - vx} = \frac{1 + v}{1 - v}.

v+xv=1+v1vv + xv' = \frac{1 + v}{1 - v}

xv=1+v1vv=1+vv+v21v=1+v21vxv' = \frac{1 + v}{1 - v} - v = \frac{1 + v - v + v^2}{1 - v} = \frac{1 + v^2}{1 - v}

1v1+v2dv=dxx\frac{1 - v}{1 + v^2}\, dv = \frac{dx}{x}

11+v2dvv1+v2dv=lnx+C\int \frac{1}{1 + v^2}\, dv - \int \frac{v}{1 + v^2}\, dv = \ln|x| + C

arctanv12ln(1+v2)=lnx+C\arctan v - \frac{1}{2}\ln(1 + v^2) = \ln|x| + C

arctan(y/x)12ln(1+y2/x2)=lnx+C\arctan(y/x) - \frac{1}{2}\ln(1 + y^2/x^2) = \ln|x| + C

arctan(y/x)=12ln(x2+y2)+C\arctan(y/x) = \frac{1}{2}\ln(x^2 + y^2) + C. \blacksquare

If you get this wrong, revise: Section 2.10 (Homogeneous Equations).

Problem 7 (Complex Roots)

Solve y+4y+13y=0y'' + 4y' + 13y = 0, y(0)=2y(0) = 2, y(0)=3y'(0) = -3.

Solution

Solution. Characteristic equation: r2+4r+13=0r^2 + 4r + 13 = 0.

r=4±16522=4±362=2±3ir = \frac{-4 \pm \sqrt{16 - 52}}{2} = \frac{-4 \pm \sqrt{-36}}{2} = -2 \pm 3i.

y=e2x(c1cos3x+c2sin3x)y = e^{-2x}(c_1 \cos 3x + c_2 \sin 3x).

y(0)=c1=2y(0) = c_1 = 2.

y=2e2x(2cos3x+c2sin3x)+e2x(6sin3x+3c2cos3x)y' = -2e^{-2x}(2\cos 3x + c_2 \sin 3x) + e^{-2x}(-6\sin 3x + 3c_2 \cos 3x).

y(0)=4+3c2=3    c2=1/3y'(0) = -4 + 3c_2 = -3 \implies c_2 = 1/3.

y=e2x(2cos3x+13sin3x)y = e^{-2x}\left(2\cos 3x + \frac{1}{3}\sin 3x\right). \blacksquare

If you get this wrong, revise: Section 3.2 (Homogeneous Equations with Constant Coefficients).

Problem 8 (Repeated Roots)

Solve y+4y+4y=0y'' + 4y' + 4y = 0, y(0)=1y(0) = 1, y(0)=0y'(0) = 0.

Solution

Solution. r2+4r+4=(r+2)2=0r^2 + 4r + 4 = (r + 2)^2 = 0. Repeated root r=2r = -2.

y=c1e2x+c2xe2xy = c_1 e^{-2x} + c_2 xe^{-2x}.

y(0)=c1=1y(0) = c_1 = 1.

y=2e2x+c2e2x2c2xe2xy' = -2e^{-2x} + c_2 e^{-2x} - 2c_2 xe^{-2x}.

y(0)=2+c2=0    c2=2y'(0) = -2 + c_2 = 0 \implies c_2 = 2.

y=e2x+2xe2x=e2x(1+2x)y = e^{-2x} + 2xe^{-2x} = e^{-2x}(1 + 2x). \blacksquare

If you get this wrong, revise: Section 3.2, Case 2.

Problem 9 (Undetermined Coefficients)

Solve y2y3y=3e2xy'' - 2y' - 3y = 3e^{2x}, y(0)=1y(0) = 1, y(0)=0y'(0) = 0.

Solution

Solution. Homogeneous: r22r3=(r3)(r+1)=0r^2 - 2r - 3 = (r - 3)(r + 1) = 0. Roots: 3,13, -1.

yh=c1e3x+c2exy_h = c_1 e^{3x} + c_2 e^{-x}.

Guess yp=Ae2xy_p = Ae^{2x}. yp=2Ae2xy_p' = 2Ae^{2x}, yp=4Ae2xy_p'' = 4Ae^{2x}.

4Ae2x4Ae2x3Ae2x=3e2x    3A=3    A=14Ae^{2x} - 4Ae^{2x} - 3Ae^{2x} = 3e^{2x} \implies -3A = 3 \implies A = -1.

y=c1e3x+c2exe2xy = c_1 e^{3x} + c_2 e^{-x} - e^{2x}.

y(0)=c1+c21=1    c1+c2=2y(0) = c_1 + c_2 - 1 = 1 \implies c_1 + c_2 = 2.

y(0)=3c1c22=0    3c1c2=2y'(0) = 3c_1 - c_2 - 2 = 0 \implies 3c_1 - c_2 = 2.

Solving: 4c1=4    c1=14c_1 = 4 \implies c_1 = 1, c2=1c_2 = 1.

y=e3x+exe2xy = e^{3x} + e^{-x} - e^{2x}. \blacksquare

If you get this wrong, revise: Section 3.6 (Undetermined Coefficients).

Problem 10 (Resonance)

Solve y+4y=8cos(2t)y'' + 4y = 8\cos(2t), y(0)=0y(0) = 0, y(0)=0y'(0) = 0.

Solution

Solution. This is resonant (ω0=2=ω\omega_0 = 2 = \omega).

yh=c1cos2t+c2sin2ty_h = c_1 \cos 2t + c_2 \sin 2t.

Guess yp=Atsin2ty_p = At\sin 2t. yp=Asin2t+2Atcos2ty_p' = A\sin 2t + 2At\cos 2t. yp=2Acos2t+2Acos2t4Atsin2t=4Acos2t4Atsin2ty_p'' = 2A\cos 2t + 2A\cos 2t - 4At\sin 2t = 4A\cos 2t - 4At\sin 2t.

yp+4yp=4Acos2t=8cos2t    A=2y_p'' + 4y_p = 4A\cos 2t = 8\cos 2t \implies A = 2.

y=c1cos2t+c2sin2t+2tsin2ty = c_1 \cos 2t + c_2 \sin 2t + 2t\sin 2t.

y(0)=c1=0y(0) = c_1 = 0. y(0)=2c2=0    c2=0y'(0) = 2c_2 = 0 \implies c_2 = 0.

y=2tsin2ty = 2t\sin 2t. \blacksquare

If you get this wrong, revise: Section 3.9 (Resonance).

Problem 11 (Reduction of Order)

Given that y1=xy_1 = x solves x2yxy+y=0x^2 y'' - xy' + y = 0 for x>0x > 0Find the general solution.

Solution

Solution. Rewrite as y1xy+1x2y=0y'' - \frac{1}{x}y' + \frac{1}{x^2}y = 0. Here p(x)=1/xp(x) = -1/x.

epdx=e1/xdx=xe^{-\int p\, dx} = e^{\int 1/x\, dx} = x.

y2=y1xy12dx=xxx2dx=x1xdx=xlnxy_2 = y_1 \int \frac{x}{y_1^2}\, dx = x \int \frac{x}{x^2}\, dx = x \int \frac{1}{x}\, dx = x \ln x.

y=c1x+c2xlnxy = c_1 x + c_2 x \ln x. \blacksquare

If you get this wrong, revise: Section 3.12 (Reduction of Order).

Problem 12 (Euler-Cauchy)

Solve x2y+3xy+y=0x^2 y'' + 3xy' + y = 0 for x>0x > 0.

Solution

Solution. Characteristic: r(r1)+3r+1=r2+2r+1=(r+1)2=0r(r-1) + 3r + 1 = r^2 + 2r + 1 = (r+1)^2 = 0.

Repeated root r=1r = -1.

y=c1x1+c2x1lnxy = c_1 x^{-1} + c_2 x^{-1}\ln x. \blacksquare

If you get this wrong, revise: Section 3.13 (Euler-Cauchy Equations).

Problem 13 (2x2 System)

Solve x=(1412)x\mathbf{x}' = \begin{pmatrix} 1 & 4 \\ 1 & -2 \end{pmatrix}\mathbf{x}.

Solution

Solution. det(AλI)=(1λ)(2λ)4=λ2+λ6=(λ+3)(λ2)=0\det(A - \lambda I) = (1 - \lambda)(-2 - \lambda) - 4 = \lambda^2 + \lambda - 6 = (\lambda + 3)(\lambda - 2) = 0.

λ1=2\lambda_1 = 2: (A2I)v=(1414)v=0(A - 2I)\mathbf{v} = \begin{pmatrix} -1 & 4 \\ 1 & -4 \end{pmatrix}\mathbf{v} = \mathbf{0}. v1=(41)\mathbf{v}_1 = \begin{pmatrix} 4 \\ 1 \end{pmatrix}.

λ2=3\lambda_2 = -3: (A+3I)v=(4411)v=0(A + 3I)\mathbf{v} = \begin{pmatrix} 4 & 4 \\ 1 & 1 \end{pmatrix}\mathbf{v} = \mathbf{0}. v2=(11)\mathbf{v}_2 = \begin{pmatrix} 1 \\ -1 \end{pmatrix}.

x(t)=c1(41)e2t+c2(11)e3t\mathbf{x}(t) = c_1 \begin{pmatrix} 4 \\ 1 \end{pmatrix} e^{2t} + c_2 \begin{pmatrix} 1 \\ -1 \end{pmatrix} e^{-3t}. \blacksquare

If you get this wrong, revise: Section 4.2 (Homogeneous Systems with Constant Coefficients).

Problem 14 (System with Complex Eigenvalues)

Solve x=(0110)x\mathbf{x}' = \begin{pmatrix} 0 & -1 \\ 1 & 0 \end{pmatrix}\mathbf{x}.

Solution

Solution. det(AλI)=λ2+1=0\det(A - \lambda I) = \lambda^2 + 1 = 0. λ=±i\lambda = \pm i.

For λ=i\lambda = i: (i11i)v=0\begin{pmatrix} -i & -1 \\ 1 & -i \end{pmatrix}\mathbf{v} = \mathbf{0}. iv1v2=0    v2=iv1-iv_1 - v_2 = 0 \implies v_2 = -iv_1. With v1=1v_1 = 1: v=(1i)=(10)+i(01)\mathbf{v} = \begin{pmatrix} 1 \\ -i \end{pmatrix} = \begin{pmatrix} 1 \\ 0 \end{pmatrix} + i\begin{pmatrix} 0 \\ -1 \end{pmatrix}.

a=(10)\mathbf{a} = \begin{pmatrix} 1 \\ 0 \end{pmatrix}, b=(01)\mathbf{b} = \begin{pmatrix} 0 \\ -1 \end{pmatrix}.

x(t)=c1(costsint)+c2(sintcost)\mathbf{x}(t) = c_1 \begin{pmatrix} \cos t \\ -\sin t \end{pmatrix} + c_2 \begin{pmatrix} \sin t \\ \cos t \end{pmatrix}.

Equivalently: x1(t)=c1cost+c2sintx_1(t) = c_1 \cos t + c_2 \sin t, x2(t)=c1sint+c2costx_2(t) = -c_1 \sin t + c_2 \cos t. \blacksquare

If you get this wrong, revise: Section 4.2, Case 3.

Problem 15 (Laplace Transform)

Compute L{t2e3t}\mathcal{L}\{t^2 e^{-3t}\}.

Solution

Solution. Using L{tneat}=n!(sa)n+1\mathcal{L}\{t^n e^{at}\} = \frac{n!}{(s-a)^{n+1}} with n=2n = 2, a=3a = -3:

L{t2e3t}=2!(s+3)3=2(s+3)3\mathcal{L}\{t^2 e^{-3t}\} = \frac{2!}{(s + 3)^3} = \frac{2}{(s+3)^3}. \blacksquare

If you get this wrong, revise: Section 5.2 (Basic Properties) and Section 5.4 (Common Transforms).

Problem 16 (IVP with Laplace)

Solve yy=ety'' - y = e^t, y(0)=0y(0) = 0, y(0)=0y'(0) = 0 using Laplace transforms.

Solution

Solution. L{yL{y}=L{et}{\mathcal{L}\{y'} - \mathcal{L}\{y\} = \mathcal{L}\{e^t\}:

s2YY=1s1s^2 Y - Y = \frac{1}{s - 1}

(s21)Y=1s1(s^2 - 1)Y = \frac{1}{s-1}

(s1)(s+1)Y=1s1(s-1)(s+1)Y = \frac{1}{s-1}

Y=1(s1)2(s+1)Y = \frac{1}{(s-1)^2(s+1)}

Partial fractions: 1(s1)2(s+1)=As1+B(s1)2+Cs+1\frac{1}{(s-1)^2(s+1)} = \frac{A}{s-1} + \frac{B}{(s-1)^2} + \frac{C}{s+1}.

1=A(s1)(s+1)+B(s+1)+C(s1)21 = A(s-1)(s+1) + B(s+1) + C(s-1)^2

s=1s = 1: 1=2B    B=1/21 = 2B \implies B = 1/2. s=1s = -1: 1=4C    C=1/41 = 4C \implies C = 1/4. s=0s = 0: 1=A+B+C=A+3/4    A=1/41 = -A + B + C = -A + 3/4 \implies A = -1/4.

Y=1/4s1+1/2(s1)2+1/4s+1Y = -\frac{1/4}{s-1} + \frac{1/2}{(s-1)^2} + \frac{1/4}{s+1}

y(t)=14et+12tet+14ety(t) = -\frac{1}{4}e^t + \frac{1}{2}te^t + \frac{1}{4}e^{-t}. \blacksquare

If you get this wrong, revise: Section 5.5 (Solving IVPs with Laplace Transforms).

Problem 17 (Inverse Laplace)

Find L1{2s+3s2+2s+5}\mathcal{L}^{-1}\left\{\frac{2s + 3}{s^2 + 2s + 5}\right\}.

Solution

Solution. Complete the square: s2+2s+5=(s+1)2+4s^2 + 2s + 5 = (s + 1)^2 + 4.

2s+3(s+1)2+4=2(s+1)+1(s+1)2+4=2s+1(s+1)2+4+122(s+1)2+4\frac{2s + 3}{(s+1)^2 + 4} = \frac{2(s+1) + 1}{(s+1)^2 + 4} = 2 \cdot \frac{s+1}{(s+1)^2 + 4} + \frac{1}{2} \cdot \frac{2}{(s+1)^2 + 4}

f(t)=2etcos2t+12etsin2t=et(2cos2t+12sin2t)f(t) = 2e^{-t}\cos 2t + \frac{1}{2}e^{-t}\sin 2t = e^{-t}\left(2\cos 2t + \frac{1}{2}\sin 2t\right). \blacksquare

If you get this wrong, revise: Section 5.8 (Worked Example: Inverse Laplace Transform).

Problem 18 (Fourier Series)

Find the Fourier series of f(x)={10<x<π1π<x<0f(x) = \begin{cases} 1 & 0 \lt x \lt \pi \\ -1 & -\pi \lt x \lt 0 \end{cases} Extended 2π2\pi-periodically (the square wave).

Solution

Solution. ff is odd, so an=0a_n = 0 for all nn.

bn=1πππf(x)sin(nx)dx=1π[π0(1)sin(nx)dx+0π(1)sin(nx)dx]b_n = \frac{1}{\pi}\int_{-\pi}^{\pi} f(x)\sin(nx)\, dx = \frac{1}{\pi}\left[\int_{-\pi}^{0}(-1)\sin(nx)\, dx + \int_0^{\pi}(1)\sin(nx)\, dx\right]

=1π[cos(nx)nπ0cos(nx)n0π]= \frac{1}{\pi}\left[\frac{\cos(nx)}{n}\Big|_{-\pi}^0 - \frac{\cos(nx)}{n}\Big|_0^{\pi}\right]

=1π[1cos(nπ)ncos(nπ)1n]=1π[22cos(nπ)n]=2(1(1)n)nπ= \frac{1}{\pi}\left[\frac{1 - \cos(n\pi)}{n} - \frac{\cos(n\pi) - 1}{n}\right] = \frac{1}{\pi}\left[\frac{2 - 2\cos(n\pi)}{n}\right] = \frac{2(1 - (-1)^n)}{n\pi}

For even nn: bn=0b_n = 0. For odd n=2k+1n = 2k + 1: bn=4nπb_n = \frac{4}{n\pi}.

f(x)4πk=0sin((2k+1)x)2k+1f(x) \sim \frac{4}{\pi}\sum_{k=0}^{\infty} \frac{\sin((2k+1)x)}{2k+1}. \blacksquare

If you get this wrong, revise: Section 7.1 and 7.6 (Fourier Series).

Problem 19 (Heat Equation)

Solve ut=4uxxu_t = 4u_{xx} for 0<x<π0 \lt x \lt \pi, t>0t > 0With u(0,t)=u(π,t)=0u(0, t) = u(\pi, t) = 0 and u(x,0)=sinxu(x, 0) = \sin x.

Solution

Solution. Here α=2\alpha = 2 and L=πL = \pi.

λn=(nπ/π)2=n2\lambda_n = (n\pi/\pi)^2 = n^2, Xn=sin(nx)X_n = \sin(nx), Tn=e4n2tT_n = e^{-4n^2 t}.

The initial condition sinx\sin x is already the first sine mode.

u(x,t)=e4tsinxu(x, t) = e^{-4t}\sin x. \blacksquare

If you get this wrong, revise: Section 8.4 (Solving the Heat Equation by Separation of Variables).

Problem 20 (Stability Classification)

Find and classify the critical points of x=yx2x' = y - x^2, y=xy2y' = x - y^2.

Solution

Solution. Set yx2=0y - x^2 = 0 and xy2=0x - y^2 = 0. From the first equation y=x2y = x^2Substituting Into the second: xx4=0x - x^4 = 0So x(1x3)=0x(1 - x^3) = 0.

x=0    y=0x = 0 \implies y = 0. Critical point: (0,0)(0, 0). x=1    y=1x = 1 \implies y = 1. Critical point: (1,1)(1, 1).

Jacobian: J=(2x112y)J = \begin{pmatrix} -2x & 1 \\ 1 & -2y \end{pmatrix}.

At (0,0)(0, 0): J=(0110)J = \begin{pmatrix} 0 & 1 \\ 1 & 0 \end{pmatrix}. tr(J)=0\mathrm{tr}(J) = 0, det(J)=1<0\det(J) = -1 \lt 0. Saddle point (unstable).

At (1,1)(1, 1): J=(2112)J = \begin{pmatrix} -2 & 1 \\ 1 & -2 \end{pmatrix}. tr(J)=4<0\mathrm{tr}(J) = -4 \lt 0, det(J)=3>0\det(J) = 3 > 0. τ24Δ=1612=4>0\tau^2 - 4\Delta = 16 - 12 = 4 > 0. Two distinct negative real eigenvalues. Stable node (asymptotically stable). \blacksquare

If you get this wrong, revise: Section 9.2 (Linearization and Stability) and Section 4.9 (Phase Portrait Analysis).

Worked Examples

Example 1: Definite integration

Evaluate 02(3x2+2x)dx\displaystyle\int_0^2 (3x^2 + 2x)\,dx.

Solution:

(3x2+2x)dx=x3+x2+c\int (3x^2 + 2x)\,dx = x^3 + x^2 + c

[x3+x2]02=(8+4)(0)=12\left[x^3 + x^2\right]_0^2 = (8 + 4) - (0) = 12

Example 2: Integration by parts

Find xe2xdx\displaystyle\int x e^{2x}\,dx.

Solution:

Let u=x    dudx=1u = x \implies \frac{du}{dx} = 1 and dvdx=e2x    v=12e2x\frac{dv}{dx} = e^{2x} \implies v = \frac{1}{2}e^{2x}.

xe2xdx=x12e2x12e2xdx=xe2x2e2x4+c=e2x(2x1)4+c\int x e^{2x}\,dx = x \cdot \frac{1}{2}e^{2x} - \int \frac{1}{2}e^{2x}\,dx = \frac{x e^{2x}}{2} - \frac{e^{2x}}{4} + c = \frac{e^{2x}(2x - 1)}{4} + c

Summary

  • First-order ODEs: separable (dydx=g(x)h(y)\frac{dy}{dx} = g(x)h(y)), linear (integrating factor μ=ePdx\mu = e^{\int P\,dx}), exact (Mdx+Ndy=0M\,dx + N\,dy = 0 with My=NxM_y = N_x).
  • Second-order linear ODEs with constant coefficients: characteristic equation ar2+br+c=0ar^2 + br + c = 0; distinct real, repeated real, or complex roots.
  • Laplace transforms: convert ODEs to algebraic equations; L{f(t)}=sF(s)f(0)\mathcal{L}\{f'(t)\} = sF(s) - f(0); particularly useful for piecewise forcing functions.
  • Systems of ODEs: x˙=Ax\dot{\mathbf{x}} = A\mathbf{x} solved via eigenvalues and eigenvectors; phase plane analysis for 2D systems.
  • PDEs: separation of variables for heat equation (ut=kuxxu_t = ku_{xx}), wave equation (utt=c2uxxu_{tt} = c^2 u_{xx}), and Laplace equation (2u=0\nabla^2 u = 0).

Cross-References

TopicSiteLink
Real AnalysisWyattsNotesView
Linear AlgebraWyattsNotesView
Multivariable CalculusWyattsNotesView
Complex AnalysisWyattsNotesView
Differential Equations — MIT 18.03MIT OCWView

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