A topology-optimized specimen under uniaxial cyclic loading generates enough local strain-path diversity in simulation to train a 2M-parameter GRU material model with ~9–13% NRMSE on unseen random paths.
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Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization
A topology-optimized specimen under uniaxial cyclic loading generates enough local strain-path diversity in simulation to train a 2M-parameter GRU material model with ~9–13% NRMSE on unseen random paths.