Lagrangian Neural Networks for Reversible Dissipative Evolution

TL;DR

This paper introduces Lagrangian Neural Networks using Morse-Feshbach Lagrangian to model dissipative systems, enabling reversible evolution in systems with frictional losses by embedding them in a higher-dimensional conservative framework.

Contribution

It presents a novel approach to modeling dissipative dynamics with Lagrangian mechanics, allowing reversible evolution through a doubled-dimensional representation.

Findings

Systems can be evolved forward and backward without additional regularization.

The approach successfully models Fickian diffusion in materials sciences.

Reversibility of dissipative dynamics demonstrated experimentally.

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 of Morse-Feshbach Lagrangian, which models dissipative dynamics by doubling the number of dimensions of the system in order to create a mirror latent representation that would counterbalance the dissipation of the observable system, making it a conservative system, albeit embedded in a larger space. We start with their formal approach by redefining a new Dissipative Lagrangian, such that the unknown matrices in the Euler-Lagrange's equations arise as partial derivatives of the Lagrangian with respect to only the observables. We then train a network from simulated training data for dissipative systems such as Fickian diffusion that arise in materials sciences. It is shown by experiments that the systems can be evolved in both forward and reverse directions without regularization beyond that provided by the Morse-Feshbach Lagrangian. Experiments of dissipative systems, such as Fickian diffusion, demonstrate the degree to which dynamics can be reversed.