AI Research
Embedding Consistent Recurrent Neural Networks in Finite Element Simulations for Path-Dependent Damage Prediction
A recurrent neural-network surrogate must give consistent predictions across varying strain-increment counts.
Capturing sheet-metal localisation physics with a trained surrogate inside a finite element (FEM) solver requires the surrogate to deliver consistent predictions irrespective of the solver's strain-increment count, which is difficult to control in non-linear explicit codes.
AC
ArXiv CS.LG
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2 min
ArXiv CS.LG
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A surrogate embedded in a finite element solver must remain reliable even when the solver changes how many strain increments it uses. Consistent predictions could help address a practical challenge in simulating sheet-metal localisation and path-dependent damage.
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