ZINZINBIN/Physics-informed-ml-study
study code for physics informed machine learning and deep learning
This project provides study code and reference materials for those looking to understand or apply physics-informed machine learning. It takes in scientific data and equations, and helps generate models that can solve complex problems in science and engineering. Researchers, scientists, and engineers working with physical systems would find this useful.
No commits in the last 6 months.
Use this if you are a researcher or engineer looking to apply deep learning to solve problems described by physical laws, especially involving partial differential equations.
Not ideal if you need a plug-and-play solution for general machine learning tasks without a strong physics component.
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Jupyter Notebook
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Last pushed
Jul 25, 2022
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