Introduction to Computational Physics: Algebra, Differential Equations and Simulations in Python

Computational physics is now a core part of undergraduate science, mathematics and engineering programs. This self-contained course emphasizes hands-on learning, reproducibility, and computational thinking within the physics context using Python. It integrates core principles outlined by the American Association of Physics Teachers and supports readers in developing a strong computational foundation for research projects.Designed with minimal prerequisites, this text equips readers with essential technical computing skills through Python programming, Jupyter notebooks, and Quarto for science reproducibility. Readers are guided through numerical algorithms, matrix algebra, data visualization, differential equations, Monte Carlo simulations, and stochastic processes. Realistic examples are drawn from classical mechanics, electricity and magnetism, statistical physics, and quantum mechanics:Computations of projectile motion with drag, driven damped mass-spring systems, Lotka-Volterra model and double pendulumNon-linear regression and data fittingUnits, uncertainties and error propagationSymbolic algebra for Lagrangian mechanicsSolving 1D heat diffusion and Schrödinger’s equationMonte Carlo simuilations and Markov chain generationBrownian motion, percolation and 2D Ising model Read more