HANO, an autoregressive Fourier neural operator with attention and U-Net branches, predicts path-dependent material stress from short observable strain-stress windows and stays accurate across loading resolutions and partial histories.
On the importance of self-consistency in recurrent neural network models representing elasto-plastic solids
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History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials
HANO, an autoregressive Fourier neural operator with attention and U-Net branches, predicts path-dependent material stress from short observable strain-stress windows and stays accurate across loading resolutions and partial histories.