Recurrent neural network-based multiaxial plasticity model with regularization for physics-informed constraints

Borkowski, L.; Sorini, C.; Chattopadhyay, A.

doi:10.1016/j.compstruc.2021.106678

Recurrent neural network-based multiaxial plasticity model with regularization for physics-informed constraints

Journal Article · Mon Sep 27 00:00:00 EDT 2021 · Computers and Structures

DOI:https://doi.org/10.1016/j.compstruc.2021.106678· OSTI ID:1977023

Borkowski, L. ^[1]; Sorini, C. ^[2]; Chattopadhyay, A. ^[2]

Raytheon Technologies Research Center, East Hartford, CT (United States); OSTI
Arizona State University, Tempe, AZ (United States)

We report a recurrent neural network (RNN) based model is developed as a surrogate to predict nonlinear plastic response under multiaxial loading. The RNN-based model is trained and tested on stress versus strain curves generated using a numerical solution based on the classical radial return method. Besides simply learning the basic constitutive relationship, a novel approach is taken to enforce certain physical conditions. Specifically, regularization is employed to maintain non-negative plastic power density throughout the loading history thereby ensuring monotonically increasing plastic work and thermodynamic consistency. Enforcing physics in this manner permits coupling of the data-driven RNN approach with physics-based knowledge and laws. This has the effect of reducing the necessary amount of data and ensuring known physical laws are not violated. Since, once trained, the model need not perform the expensive task of solving nonlinear equations, its efficiency is orders of magnitude greater than its numerical counterpart. The RNN-based model has been trained on varied sets of data and the accuracy on test datasets validated. The developed model is general and robust and has widespread application such as in the simulation of metal forming, large scale plasticity, and part life prediction.

View Accepted Manuscript (DOE)

Research Organization:: Arizona State University, Tempe, AZ (United States)

Sponsoring Organization:: USDOE; USDOE Office of Fossil Energy (FE)

Grant/Contract Number:: FE0031759

OSTI ID:: 1977023

Alternate ID(s):: OSTI ID: 2396343
OSTI ID: 1868706

Report Number(s):: DOE-0031759; PII: S0045794921002005

Journal Information:: Computers and Structures, Journal Name: Computers and Structures Journal Issue: C Vol. 258; ISSN 0045-7949

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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