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The Ethereal
AI Poincarรฉ: Machine Learning Conservation Laws from Trajectories
November 09, 2020 ยท Declared Dead ยท ๐ Physical Review Letters
"No code URL or promise found in abstract"
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Authors
Ziming Liu, Max Tegmark
arXiv ID
2011.04698
Category
cs.LG: Machine Learning
Cross-listed
astro-ph.EP,
nlin.SI,
physics.class-ph
Citations
135
Venue
Physical Review Letters
Last Checked
4 months ago
Abstract
We present AI Poincarรฉ, 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.
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