Computational Physics
Numerical integration, ODEs, root finding, derivatives, linear systems, eigenvalues, and Monte Carlo.
Numerical Methods
Computational tools for physics problems
Gradient Descent
Iteratively minimize f(x) by following the negative gradient with animated path visualization.
1D Heat Equation
Finite-difference FTCS solution to the diffusion equation with animated temperature profiles.
1D Wave Equation
Leapfrog finite-difference solution to the wave equation with animated wave propagation.
Numerical Integration
Trapezoidal and Simpson rules to approximate definite integrals with error vs exact solutions.
ODE Solver
Euler and Runge-Kutta 4 methods for first-order ODEs with comparison to analytic solutions.
Monte Carlo Intro
Estimate π and integrals by random sampling — introduction to stochastic computational physics.
Newton-Raphson
Solve nonlinear equations f(x) = 0 with tangent-line iterations — fast when the guess is good.
Bisection Method
Bracket and halve intervals to find roots — guaranteed convergence with a sign change.
Finite Differences
Approximate derivatives with forward, backward, and central stencils on physics functions.
Linear System Solver
Solve 2×2 coupled equations from circuits and equilibrium — determinant and residuals.
Power Method
Estimate dominant eigenvalues and mode directions for coupled systems.
Quick practice quiz
Test your understanding — tap an option, then open the linked calculator to explore.
Bisection method requires:
Open calculator →