Simplified Computational Physics: Lecture notes & handouts for Beginner’s course on Computational Physics, with Lab sessions in Python

The notes begin by framing computation as a third pillar of physics, alongside theory and experiment – capable of reproducing controlled conditions, minimizing measurement error, and extending models into regimes inaccessible to analytic methods. Early chapters introduce the essentials: floating-point arithmetic, precision errors, and the use of Python as a transparent medium for scientific thinking.

Subsequent sections cover regression, interpolation, differentiation, and numerical integration as the foundational building blocks of computational reasoning. Progressing into dynamics, the text develops step-by-step derivations of standard numerical schemes: Forward Time Centered Space (FTCS), Forward Time Forward Space (FTFS), Lax, Lax-Friedrichs, and Lax-Wendroff, each accompanied by the rationale behind its stability and accuracy. The Von-Neumann stability analysis and the Courant-Friedrichs-Lewy (CFL) condition are presented not as abstract theorems but as physical constraints ensuring that numerical propagation remains causal and consistent with the real world. Throughout, examples are compact, visual, and executable in Python, allowing readers to see numerical ideas come to life. The document is intentionally structured as a lecture-note compilation and quick reference handbook rather than a full textbook – ideal for last-minute revision, laboratory support, or a first immersion in scientific computing.

Ultimately, this work invites students to treat computation not as a technical chore but as a means of exploration: an experimental arm of the mind that lets physics be both calculated and experienced