Lagrangian Neural Networks for Reversible Dissipative Evolution

arXiv:2405.14645v1 Announce Type: new
Abstract: There is a growing attention given to utilizing Lagrangian and Hamiltonian mechanics with network training in order to incorporate physics into the network. Most commonly, conservative systems are modeled, in which there are no frictional losses, so the system may be run forward and backward in time without requiring regularization. This work addresses systems in which the reverse direction is ill-posed because of the dissipation that occurs in forward evolution. The novelty is the use …

abstract arxiv attention cond-mat.mtrl-sci cs.lg evolution losses network networks network training neural networks physics systems training type