Deep learning-based computational method for soft matter dynamics: Deep Onsager-Machlup method

TL;DR

The paper introduces the Deep Onsager-Machlup method (DOMM), a deep learning approach that combines neural networks with physical principles to accurately simulate complex soft matter dynamics involving multiple physical processes.

Contribution

It extends the deep Ritz method by integrating the Onsager-Machlup variational principle, enabling the solution of complex soft matter dynamic problems with multiple scales and dissipative processes.

Findings

DOMM accurately predicts particle diffusion and two-phase dynamics.

Results agree well with analytical and traditional numerical solutions.

Demonstrates convergence and potential of DOMM as an alternative computational method.

Abstract

A deep learning-based computational method is proposed for soft matter dynamics -- the deep Onsager-Machlup method (DOMM). It combines the brute forces of deep neural networks (DNNs) with the fundamental physics principle -- Onsager-Machlup variational principle (OMVP). In the DOMM, the trial solution to the dynamics is constructed by DNNs that allow us to explore a rich and complex set of admissible functions. It outperforms the Ritz-type variational method where one has to impose carefully-chosen trial functions. This capability endows the DOMM with the potential to solve rather complex problems in soft matter dynamics that involve multiple physics with multiple slow variables, multiple scales, and multiple dissipative processes. Actually, the DOMM can be regarded as an extension of the deep Ritz method (DRM) developed by E and Yu that uses DNNs to solve static problems in physics. In this work, as the first step, we focus on the validation of the DOMM as a useful computational method by using it to solve several typical soft matter dynamic problems: particle diffusion in dilute solutions, and two-phase dynamics with and without hydrodynamics. The predicted results agree very well with the analytical solution or numerical solution from traditional computational methods. These results show the accuracy and convergence of DOMM and justify it as an alternative computational method for solving soft matter dynamics.