Accelerating elastoplastic material models with spare nonlinear regression: A hybrid approach
Evaluating material models in Finite Element (FE) simulations is computationally expensive. Recently, Machine Learning (ML) techniques have been explored for accelerating elastoplastic algorithms. One such method includes replacing a part of the algorithm with an ML model which is called the “hybrid” approach. One of the most commonly used algorithms for ductile materials is the J2-based von Mises hardening elastoplasticity. To improve the performance of this model, an ML-based hybrid algorithm was sought. In this algorithm, the expensive iterative plastic correction step was replaced with a single-step prediction from a SINDY-inspired sparse nonlinear regression model.
https://www.dynalook.com/conferences/14th-european-ls-dyna-conference-2023/machine-learning-ai/bokil_dlr.pdf/view
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Accelerating elastoplastic material models with spare nonlinear regression: A hybrid approach
Evaluating material models in Finite Element (FE) simulations is computationally expensive. Recently, Machine Learning (ML) techniques have been explored for accelerating elastoplastic algorithms. One such method includes replacing a part of the algorithm with an ML model which is called the “hybrid” approach. One of the most commonly used algorithms for ductile materials is the J2-based von Mises hardening elastoplasticity. To improve the performance of this model, an ML-based hybrid algorithm was sought. In this algorithm, the expensive iterative plastic correction step was replaced with a single-step prediction from a SINDY-inspired sparse nonlinear regression model.
Bokil_DLR.pdf
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