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Addressing materials' microstructure diversity using transfer learning
We show that a state-of-the-art UDA approach surpasses the na\"ive application of source domain trained models on the target domain (generalization baseline) to a large extent.
A Bayesian multiscale CNN framework to predict local stress fields in structures with microscale features
Multiscale computational modelling is challenging due to the high computational cost of direct numerical simulation by finite elements.
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