Recurrent neural network-based multiaxial plasticity model with regularization for physics-informed constraints. (1st January 2022)

• Record Type: Journal Article
• Authors: Borkowski, L.; Sorini, C.; Chattopadhyay, A.
• Is Part Of: Computers & structures. Volume 258(2022)
• Publication Date: 2022-01-01
• Subjects: Constitutive behavior -- Elastic-plastic material -- Recurrent neural network (RNN) -- Surrogate model -- Regularization; Structural engineering -- Data processing -- Periodicals; Electronic data processing -- Structures, Theory of -- Periodicals; 624.171
• DOI: 10.1016/j.compstruc.2021.106678
• Languages: English
• ISSNs: 0045-7949

Abstract

Highlights: Recurrent neural network-based surrogate model for nonlinear plasticity presented. Regularization employed to enforce thermodynamic consistency. Surrogate model accurately represents multiaxial cyclic loading material response. Efficiency over classical numerical approach improved several orders of magnitude. Abstract: 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.