A VRNN-DIRT framework with tensor trains delivers low-variance failure probability estimates for 3D heterogeneous composites in dimensions up to 150.
Geometric deep learning for computational mechanics part i: Anisotropic hyperelasticity.Computer Methods in Applied Mechanics and Engineering, 371:113299, 2020
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Multiscale Structural Reliability Analysis in high dimensions with Tensor Trains and Physics-Augmented Neural Networks
A VRNN-DIRT framework with tensor trains delivers low-variance failure probability estimates for 3D heterogeneous composites in dimensions up to 150.