SciML Notebook

Companion Jupyter notebooks for Scientific Machine Learning: Foundations, Methods, and Applications by Krishna Kumar.

Each notebook implements the methods and examples discussed in the textbook. The code uses PyTorch and JAX.

Chapters

• Physics-Informed Neural Networks (Chapter 6). Harmonic oscillator, Poisson equation, strong/weak/energy forms, collocation, adaptive weights, boundary conditions, Burgers equation, spectral bias, optimizer benchmarks.