IBM/simulai
A toolkit with data-driven pipelines for physics-informed machine learning.
This toolkit helps researchers and engineers build physics-informed machine learning models to simulate complex physical systems. You input observational data and known physical laws, and it outputs predictive models that adhere to scientific principles. It's designed for scientists, physicists, and engineers working with simulations, fluid dynamics, or material science.
197 stars. No commits in the last 6 months.
Use this if you need to develop machine learning models that not only learn from data but also respect the underlying physical laws of the system you are studying.
Not ideal if your problem domain does not involve physical systems or if you require models that are purely data-driven without incorporating physical constraints.
Stars
197
Forks
28
Language
Python
License
Apache-2.0
Category
Last pushed
Sep 17, 2025
Commits (30d)
0
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