Learning and Interpreting Potentials for Classical Hamiltonian Systems
From MaRDI portal
Publication:6322766
arXiv1907.11806MaRDI QIDQ6322766
Author name not available (Why is that?)
Publication date: 26 July 2019
Abstract: We consider the problem of learning an interpretable potential energy function from a Hamiltonian system's trajectories. We address this problem for classical, separable Hamiltonian systems. Our approach first constructs a neural network model of the potential and then applies an equation discovery technique to extract from the neural potential a closed-form algebraic expression. We demonstrate this approach for several systems, including oscillators, a central force problem, and a problem of two charged particles in a classical Coulomb potential. Through these test problems, we show close agreement between learned neural potentials, the interpreted potentials we obtain after training, and the ground truth. In particular, for the central force problem, we show that our approach learns the correct effective potential, a reduced-order model of the system.
Has companion code repository: https://github.com/hbhat4000/learningpotentials
This page was built for publication: Learning and Interpreting Potentials for Classical Hamiltonian Systems
Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6322766)
