A time multiscale based data-driven approach in cyclic elasto-plasticity

TL;DR

This paper introduces a data-driven, multiscale time approach for modeling cyclic elasto-plasticity, significantly reducing computational time by leveraging macro-micro time decomposition and sparse sampling.

Contribution

It develops a novel predictor-corrector model using multi-time PGD for efficient simulation of cyclic elasto-plastic behavior.

Findings

Significant reduction in computational time.

Effective macro-micro time decomposition.

Accurate prediction of nonlinear responses.

Abstract

Within the framework of computational plasticity, recent advances show that the quasi-static response of an elasto-plastic structure under cyclic loadings may exhibit a time multiscale behaviour. In particular, the system response can be computed in terms of time microscale and macroscale modes using a weakly intrusive multi-time Proper Generalized Decomposition (MT-PGD). In this work, such micro-macro characterization of the time response is exploited to build a data-driven model of the elasto-plastic constitutive relation. This can be viewed as a predictor-corrector scheme where the prediction is driven by the macrotime evolution and the correction is performed via a sparse sampling in space. Once the nonlinear term is forecasted, the multi-time PGD algorithm allows the fast computation of the total strain. The algorithm shows considerable gains in terms of computational time, opening new perspectives in the numerical simulation of history-dependent problems defined in very large time intervals.