Towards out of distribution generalization for problems in mechanics. (arXiv:2206.14917v2 [stat.ML] UPDATED)

There has been a massive increase in research interest towards applying data
driven methods to problems in mechanics. While traditional machine learning
(ML) methods have enabled many breakthroughs, they rely on the assumption that
the training (observed) data and testing (unseen) data are independent and
identically distributed (i.i.d). Thus, traditional ML approaches often break
down when applied to real world mechanics problems with unknown test
environments and data distribution shifts. In contrast, out-of-distribution
(OOD) generalization assumes that the test data …

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