jdtoscano94/NABLA-SciML
Physics Informed Machine Learning Tutorials (Pytorch and Jax)
This project provides advanced machine learning tools to help researchers and scientists analyze complex physical systems that are difficult to study with traditional methods. It takes experimental data or mathematical descriptions of physical phenomena and produces predictions or insights into system behavior, such as fluid flows or turbulent convection. This is for computational scientists, engineers, and physicists researching intricate physical processes.
649 stars.
Use this if you are developing or applying physics-informed machine learning models to understand complex systems like fluid dynamics or biological flows.
Not ideal if you need a general-purpose machine learning library for tasks outside of scientific machine learning or complex physical system modeling.
Stars
649
Forks
198
Language
Jupyter Notebook
License
—
Category
Last pushed
Mar 10, 2026
Commits (30d)
0
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