Abstract
We present AI Poincaré, a machine learning algorithm for autodiscovering conserved quantities using trajectory data from unknown dynamical systems. We test it on five Hamiltonian systems, including the gravitational three-body problem, and find that it discovers not only all exactly conserved quantities, but also periodic orbits, phase transitions, and breakdown timescales for approximate conservation laws.
- Received 9 November 2020
- Revised 20 January 2021
- Accepted 15 April 2021
DOI:https://doi.org/10.1103/PhysRevLett.126.180604
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