A heteroencoder architecture for prediction of failure locations in porous metals using variational inference

@article{Bridgman2022AHA,
  title={A heteroencoder architecture for prediction of failure locations in porous metals using variational inference},
  author={Wyatt Bridgman and Xiaoxuan Zhang and Gregory H. Teichert and M. Khalil and Krishna C. Garikipati and Reese Jones},
  journal={ArXiv},
  year={2022},
  volume={abs/2202.00078}
}

Geometric deep learning for computational mechanics Part II: Graph embedding for interpretable multiscale plasticity

TLDR
Geometric learning-based encoding on graphs allows the embedding of rich time-history data onto a lowdimensional Euclidean space such that the evolution of plastic deformation can be predicted in the embedded feature space.

Deep Convolutional Ritz Method: Parametric PDE surrogates without labeled data

TLDR
Surrogates generated from DCRM converge significantly faster than their CPINN counterparts and prove to generalize faster and better than surrogates obtained from both CNNs trained on labeled data and CPINNs, suggesting that DCRM could make PDE solution surrogates trained without labeled data possible.

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Machine learning materials physics: Integrable deep neural networks enable scale bridging by learning free energy functions

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