AI Poincar\'e: Machine Learning Conservation Laws from Trajectories

• URL: http://arxiv.org/abs/2011.04698v2
• Date: Mon, 26 Apr 2021 13:20:29 GMT
• Title: AI Poincar\'e: Machine Learning Conservation Laws from Trajectories
• Authors: Ziming Liu (MIT), Max Tegmark (MIT)
• Abstract summary: We present AI Poincar'e, a machine learning algorithm for auto-discovering conserved quantities. We test it on five Hamiltonian systems, including the gravitational 3-body problem.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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