Deep autoencoders for physics-constrained data-driven nonlinear materials modeling

TL;DR

This paper introduces a deep autoencoder-based framework for physics-constrained data-driven nonlinear materials modeling, improving robustness and extrapolative capabilities by reducing dimensionality and enhancing stability.

Contribution

It presents a novel autoencoder architecture integrated into a physics-constrained data-driven approach for nonlinear materials, addressing high-dimensionality and generalization challenges.

Findings

Effective low-dimensional embedding of material data

Enhanced robustness and predictability in material modeling

Demonstrated applicability on nonlinear biological tissues

Abstract

Physics-constrained data-driven computing is an emerging computational paradigm that allows simulation of complex materials directly based on material database and bypass the classical constitutive model construction. However, it remains difficult to deal with high-dimensional applications and extrapolative generalization. This paper introduces deep learning techniques under the data-driven framework to address these fundamental issues in nonlinear materials modeling. To this end, an autoencoder neural network architecture is introduced to learn the underlying low-dimensional representation (embedding) of the given material database. The offline trained autoencoder and the discovered embedding space are then incorporated in the online data-driven computation such that the search of optimal material state from database can be performed on a low-dimensional space, aiming to enhance the robustness and predictability with projected material data. To ensure numerical stability and representative constitutive manifold, a convexity-preserving interpolation scheme tailored to the proposed autoencoder-based data-driven solver is proposed for constructing the material state. In this study, the applicability of the proposed approach is demonstrated by modeling nonlinear biological tissues. A parametric study on data noise, data size and sparsity, training initialization, and model architectures, is also conducted to examine the robustness and convergence property of the proposed approach.