Teaching

Computational Physics: A Modern Introduction

Lecture notes for PHY 461 / 611 · Rutgers University-Newark · by Li Zhu

A self-contained, project-based introduction to numerical methods and modern machine learning for physics. The book walks from Python fundamentals through numerical integration, differentiation, Fourier analysis, differential equations, Monte Carlo methods, and optimization, and closes with four chapters on neural networks, deep learning, and graph neural networks applied to physical problems. All examples are executable and the source is built with Jupyter Book.