greydanus/hamiltonian-nn
Code for our paper "Hamiltonian Neural Networks"
This project helps researchers and scientists model physical systems where energy conservation is critical. It takes observed data of a system's motion (like a pendulum or planetary orbits) and produces a neural network model that accurately predicts future movements while inherently respecting conservation laws. The primary users are researchers in physics, dynamics, or machine learning who need robust, physically-informed simulations.
510 stars. No commits in the last 6 months.
Use this if you need to build predictive models for physical systems and want to ensure those models respect fundamental conservation laws, leading to more stable and accurate long-term simulations.
Not ideal if your system's dynamics do not involve conservation laws or if you are working with non-physical data domains.
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510
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151
Language
Jupyter Notebook
License
Apache-2.0
Category
Last pushed
Apr 13, 2021
Commits (30d)
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