Physics Informed Deep Learning for Strain Gradient Continuum Plasticity

TL;DR

This paper introduces a physics-informed deep learning approach to efficiently solve stiff strain gradient plasticity models, overcoming limitations of traditional finite element methods in handling complex, propagating plastic bands.

Contribution

The paper develops novel modifications to PIDL loss functions for rate-dependent plasticity, enabling accurate solutions of stiff PDEs and boundary conditions in strain gradient models.

Findings

PIDL effectively handles stiff PDEs in plasticity models.

The method reduces computational effort compared to FE methods.

Flexible implementation allows customization for specific problems.

Abstract

We use a space-time discretization based on physics informed deep learning (PIDL) to approximate solutions of a class of rate-dependent strain gradient plasticity models. The differential equation governing the plastic flow, the so-called microforce balance for this class of yield-free plasticity models, is very stiff, often leading to numerical corruption and a consequent lack of accuracy or convergence by finite element (FE) methods. Indeed, setting up the discretized framework, especially with an elaborate meshing around the propagating plastic bands whose locations are often unknown a-priori, also scales up the computational effort significantly. Taking inspiration from physics informed neural networks, we modify the loss function of a PIDL model in several novel ways to account for the balance laws, either through energetics or via the resulting PDEs once a variational scheme is applied, and the constitutive equations. The initial and the boundary conditions may either be imposed strictly by encoding them within the PIDL architecture, or enforced weakly as a part of the loss function. The flexibility in the implementation of a PIDL technique often makes for its ready interface with powerful optimization schemes, and this in turn provides for many possibilities in posing the problem. We have used freely available open-source libraries that perform fast, parallel computations on GPUs. Using numerical illustrations, we demonstrate how PIDL methods could address the computational challenges posed by strain gradient plasticity models. Also, PIDL methods offer abundant potentialities, vis-\'a-vis a somewhat straitjacketed and poorer approximant of FE methods, in customizing the formulation as per the problem objective.