A physics-guided POD-DeepONet surrogate predicts microscale displacements in viscoelastic composites with about 2-5% field errors and about 100x speedup over the reference FE solver.
Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to dynamical systems
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A Neural Operator based Hybrid Microscale Model for Multiscale Simulation of Rate-Dependent Materials
A physics-guided POD-DeepONet surrogate predicts microscale displacements in viscoelastic composites with about 2-5% field errors and about 100x speedup over the reference FE solver.