AI Poincaré: Machine Learning Conservation Laws from Trajectories

Kavli Affiliate: Max Tegmark

| First 5 Authors: Ziming Liu, Max Tegmark, , ,

| Summary:

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.

| Search Query: ArXiv Query: search_query=au:”Max Tegmark”&id_list=&start=0&max_results=10

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