dandip/ssinn
Code for the paper "Sparse Symplectically Integrated Neural Networks"
This project helps scientists and engineers discover the underlying physical laws of a dynamic system from observational data. It takes in time-series measurements of a system's behavior and outputs a compact, interpretable mathematical model that describes how the system evolves over time. This tool is ideal for researchers in physics, engineering, and related fields who need to understand complex dynamic phenomena.
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Use this if you need to derive the governing equations for a physical system from limited and potentially noisy observational data, especially for systems exhibiting conservation properties like energy.
Not ideal if your system's dynamics are not governed by Hamiltonian mechanics or if you are looking for a black-box predictive model rather than an interpretable physical law.
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Python
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Last pushed
Oct 20, 2020
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