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A time multiscale based data-driven approach in cyclic elasto-plasticity
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DOI：10.1016/j.compstruc.2024.107277.png)
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Abstract
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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 behavior. In particular, the system response can be computed in terms of time microscale and macroscale modes using a weakly intrusive multitime 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 forecast, 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.
Keywords:
Model-order reduction
Multi-time PGD
Higher-order DMD
Nonlinear behavior forecasting
Data completion
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