gaoliyao/BayesianSindyAutoencoder
Bayesian autoencoders for data-driven discovery of coordinates, governing equations and fundamental constants. Proceedings of the Royal Society A.
This project helps scientists and engineers discover the underlying physical laws and fundamental constants from complex, noisy experimental data. By inputting raw measurement data, such as videos of physical systems or reaction-diffusion patterns, it outputs simplified coordinates and the governing equations that describe the system's behavior. This is ideal for researchers in physics, engineering, and applied mathematics.
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Use this if you need to extract the fundamental equations and coordinate systems from observational data in physics or engineering, especially when dealing with high-dimensional or noisy measurements.
Not ideal if you already have a well-defined physical model and only need to fit parameters, or if your data is not related to dynamic systems governed by differential equations.
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Jupyter Notebook
License
MIT
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Last pushed
Jul 09, 2024
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