%0 Generic %1 liu2020poincare %A Liu, Ziming %A Tegmark, Max %D 2020 %K tifr %T AI Poincaré: Machine Learning Conservation Laws from Trajectories %U http://arxiv.org/abs/2011.04698 %X We present AI Poincaré, 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.

@misc{liu2020poincare, 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.}, added-at = {2020-11-11T07:00:43.000+0100}, author = {Liu, Ziming and Tegmark, Max}, biburl = {https://www.bibsonomy.org/bibtex/22b9cc9b4ada149fbbeadeabaa86420d8/citekhatri}, description = {AI Poincar\'e: Machine Learning Conservation Laws from Trajectories}, interhash = {504fff96b01fca9b0bc382d784fadda8}, intrahash = {2b9cc9b4ada149fbbeadeabaa86420d8}, keywords = {tifr}, note = {cite arxiv:2011.04698Comment: 5 pages, 3 figs}, timestamp = {2020-11-11T07:00:43.000+0100}, title = {AI Poincar\'e: Machine Learning Conservation Laws from Trajectories}, url = {http://arxiv.org/abs/2011.04698}, year = 2020 }