nPIML-team/nPIML
The official respository for noise-aware physics-informed machine learning (nPIML)
This project helps applied scientists and researchers discover the underlying partial differential equations (PDEs) that govern their observed physical systems, even when measurements are noisy. You input raw, noisy observational data from experiments or simulations, and it outputs the specific PDE that best describes the system's behavior. This is ideal for physicists, engineers, and mathematicians working with complex dynamic systems.
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Use this if you need to identify the mathematical equations governing a physical phenomenon from noisy measurement data.
Not ideal if you already know the governing equations and just need to solve them, or if your data is perfectly clean and noise-free.
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TeX
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MIT
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Last pushed
Dec 16, 2024
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