Deep autoencoders for nonlinear physics-constrained data-driven computational framework with application to biological tissue modeling

Xiaolong He, Qizhi He, Jiun Shyan Chen

Research output: Contribution to journalConference articlepeer-review

Abstract

Physics-constrained data-driven computing is an emerging paradigm that directly integrates material database into physical simulations of complex materials, bypassing the construction of classical constitutive models. However, most of the developed data-driven computing approaches are based on simplistic distance minimization and thus suffer from dealing with high-dimensional applications and lack generalization ability. This study proposes a deep learning enhanced data-driven computing framework to address these fundamental issues for nonlinear materials modeling. To this end, an autoencoder, a special multi-layer neural network architecture, is introduced to learn the underlying low-dimensional embedding representation of the material database. Incorporating the offline trained autoencoder and the discovered embedding space in online data-driven computation enables to search for the optimal material state from database in low-dimensional embedding space, enhancing the robustness and predictability of data-driven computing on limited material data. To enhance stability and convergence of data-driven computing, a convexity-preserving interpolation scheme is introduced for constructing the material state on the low-dimensional embedding space given by autoencoders. The effectiveness and enhanced generalization performance of the proposed approach are examined by modeling biological tissue with experimental data.

Original languageEnglish (US)
Article number67
JournalCEUR Workshop Proceedings
Volume2964
StatePublished - 2021
Externally publishedYes
EventAAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences, AAAI-MLPS 2021 - Stanford, United States
Duration: Mar 22 2021Mar 24 2021

Bibliographical note

Publisher Copyright:
Copyright © 2021for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

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