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second-order-odes

Provides mathematical strategies and code tools to solve second-order ordinary differential equations.

Install

mkdir -p .claude/skills/second-order-odes && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/8413" && unzip -o skill.zip -d .claude/skills/second-order-odes && rm skill.zip

Installs to .claude/skills/second-order-odes

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Problem-solving strategies for second order odes in odes pdes
61 charsno explicit “when” trigger
Advanced

Key capabilities

  • Classify second-order ODEs
  • Solve homogeneous constant coefficient equations
  • Compute particular solutions
  • Perform numerical integration
  • Solve boundary value problems

How it works

It classifies the ODE type and applies specific mathematical strategies like characteristic equations or numerical solvers via Scipy and Sympy.

Inputs & outputs

You give it
ODE equation and boundary conditions
You get back
Symbolic or numerical solution

When to use second-order-odes

  • Solve constant coefficient homogeneous equations
  • Compute particular solutions for non-homogeneous ODEs
  • Solve boundary value problems using the shooting method
  • Convert second-order ODEs to first-order systems

About this skill

Second Order Odes

When to Use

Use this skill when working on second-order-odes problems in odes pdes.

Decision Tree

  1. Classify the ODE

    • Constant coefficients: ay'' + by' + cy = f(x)?
    • Variable coefficients: y'' + P(x)y' + Q(x)y = R(x)?
    • Cauchy-Euler: x^2 y'' + bxy' + cy = 0?
  2. Homogeneous with Constant Coefficients

    • Characteristic equation: ar^2 + br + c = 0
    • Distinct real roots: y = c1e^{r1x} + c2e^{r2x}
    • Repeated root: y = (c1 + c2x)e^{rx}
    • Complex roots a +/- bi: y = e^{ax}(c1cos(bx) + c2sin(bx))
    • sympy_compute.py solve "a*r**2 + b*r + c" --var r
  3. Particular Solution (Non-homogeneous)

    • Undetermined coefficients: guess based on f(x)
    • Variation of parameters: y_p = u1y1 + u2y2
    • sympy_compute.py dsolve "y'' + y = sin(x)"
  4. Numerical Solution

    • Convert to first-order system: let v = y', then v' = y''
    • solve_ivp(system, [t0, tf], [y0, v0])
  5. Boundary Value Problems

    • Shooting method: guess initial slope, iterate
    • scipy.integrate.solve_bvp(ode, bc, x, y_init)

Tool Commands

Scipy_Solve_Ivp_System

uv run python -c "from scipy.integrate import solve_ivp; sol = solve_ivp(lambda t, Y: [Y[1], -Y[0]], [0, 10], [1, 0]); print('y(10) =', sol.y[0][-1])"

Sympy_Charpoly

uv run python -m runtime.harness scripts/sympy_compute.py solve "r**2 + r + 1" --var r

Sympy_Dsolve_2Nd

uv run python -m runtime.harness scripts/sympy_compute.py dsolve "Derivative(y,x,2) + y"

Key Techniques

From indexed textbooks:

  • [An Introduction to Numerical Analysis... (Z-Library)] Modern Numerical Methods for Ordinary Wiley, New York. User's guide for DVERK: A subroutine for solving non-stiff ODEs. Keller (1966), Analysis of Numerical Methods.
  • [Elementary Differential Equations and... (Z-Library)] Riccati equation and that y1(t) = 1 is one solution. Use the transformation suggested in Problem 33, and nd the linear equation satised by v(t). Find v(t) in the case that x(t) = at, where a is a constant.
  • [An Introduction to Numerical Analysis... (Z-Library)] Test results on initial value methods for non-stiff ordinary differential equations, SIAM J. Comparing numerical methods for Fehlberg, E. Klassische Runge-Kutta-Formeln vierter und niedrigerer Ordnumg mit Schrittweiten-Kontrolle und ihre Anwendung auf Warme leitungsprobleme, Computing 6, 61-71.
  • [Elementary Differential Equations and... (Z-Library)] Two papers by Robert May cited in the text are R. May,“Biological Populations with Nonoverlapping Generations: Stable Points, Stable Cycles, and Chaos,” Science 186 (1974), pp. Biological Populations Obeying Difference Equations: Stable Points, Stable Cycles, and Chaos,” Journal of Theoretical Biology 51 (1975), pp.
  • [An Introduction to Numerical Analysis... (Z-Library)] COLSYS: collocation software for boundary-value ODEs, ACM Trans. Numerical Solutions of Boundary Value Problems for Ordinary Differential Equations. Elementary Differential Equations and Boundary Value Problems, 4th ed.

Cognitive Tools Reference

See .claude/skills/math-mode/SKILL.md for full tool documentation.

When not to use it

  • For first-order ODEs
  • For partial differential equations

Prerequisites

sympyscipy

Limitations

  • Requires classification before solving
  • Numerical methods require conversion to first-order systems

How it compares

It automates the classification and selection of resolution strategies compared to manual derivation.

Compared to similar skills

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