Discovering Conservation Laws using Optimal Transport and Manifold Learning. (arXiv:2208.14995v1 [physics.comp-ph])

Conservation laws are key theoretical and practical tools for understanding,
characterizing, and modeling nonlinear dynamical systems. However, for many
complex dynamical systems, the corresponding conserved quantities are difficult
to identify, making it hard to analyze their dynamics and build efficient,
stable predictive models. Current approaches for discovering conservation laws
often depend on detailed dynamical information, such as the equation of motion
or fine-grained time measurements, with many recent proposals also relying on
black box parametric deep learning methods. We instead …

arxiv conservation laws learning manifold physics transport