Machine Learning Conservation Laws from Trajectories
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Show full item recordAbstract
We present AI Poincar\'e, a machine learning algorithm for auto-discovering
conserved quantities using trajectory data from unknown dynamical systems. We
test it on five Hamiltonian systems, including the gravitational 3-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.
Date issued
2021-05Department
Massachusetts Institute of Technology. Department of Physics; Center for Brains, Minds, and MachinesJournal
Physical Review Letters
Publisher
American Physical Society (APS)
Citation
Liu, Ziming and Tegmark, Max. 2021. "Machine Learning Conservation Laws from Trajectories." Physical Review Letters, 126 (18).
Version: Final published version
ISSN
0031-9007
1079-7114