Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications

This book is a compilation of lecture notes from the MIT Course 18.337J/6.338J: Parallel Computing and Scientific Machine Learning. Links to the old notes https://mitmath.github.io/18337 will redirect here.

This repository is meant to be a live document, updating to continuously add the latest details on methods from the field of scientific machine learning and the latest techniques for high-performance computing.

To view this book, go to book.sciml.ai.

Editing the SciML Book

This is a Quarto book. Prose is authored in Quarto Mardown (qmd) files or Jupyter (ipynb) notebooks and code execution occurs via the Jupyter engine. For most efficient editing experience, precompile a system image and build a custom Jupyter kernel named scimlbook-1.8. This is only done once.

First, precomile a system image for the book project.

1. Open SciMLBook in VS Code
2. Open command pallete (Ctrl + Shift + P)
3. Tasks: Run Build Task
4. Julia: Build custom sysimage for current environment (will take > 20 minutes)
5. Fire up a Julia REPL
6. using IJulia
7. IJulia.installkernel("SciMLBook", "--project=path/to/SciMLBook --sysimage=/path/to/SciMLBook/JuliaSysimage.(so|dll)")
8. Open SciMLBook in VS Code
9. Open any *.qmd file
10. Click Render

Todo

• Transition all references to appendix with BibTex format
• Move YouTube videos to common (suplemental resources)location
• Update Juno.profiler() to equivalent VS Code version