Tip
You may directly jump in here.
This book aims to be an approachable introduction to simulation methods (with a slight focus on fluid dynamics) and computational statistics leading up to machine learning.
Caution
These are personal lecture notes, not a polished, errata-free textbook - in fact it will contain many mistakes. It is a work in progress, please raise issues or pull requests if you find any mistakes or have suggestions for improvements.
If you are interested in expanding the book, here are some suggestions for topics which I feel could be added:
- Hamilton Monte Carlo / NUTS
- Lattice Boltzmann fluid dynamics
- Average Vector Field method for ODEs/PDEs
- pretty much everything Scientific Machine Learning related with a critical review of the literature
If you're here, you might also be interested in
- The Elements of Differentiable Programming
- Physics-based Deep Learning Book
- Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications
or my small astro fluid simulator jf1uids.