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

  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},

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

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

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.



Predicting the mechanical response of oligocrystals with deep learning

Deep Learning for Stress Field Prediction Using Convolutional Neural Networks

This research presents a deep learning based approach to predict stress fields in the solid material elastic deformation using convolutional neural networks (CNN). Two different architectures are

Mesh-based graph convolutional neural networks for modeling materials with microstructure

This work proposes a graph convolutional neural network that utilizes the discretized representation of the initial microstructure directly, without segmentation or clustering, and demonstrates the performance of the proposed network to traditional pixel-based convolution neural network models and feature-based graph convolutionsal neural networks on multiple large datasets.

Bayesian neural networks for weak solution of PDEs with uncertainty quantification

A new physics-constrained neural network (NN) approach is proposed to solve PDEs without labels, with a view to enabling high-throughput solutions in support of design and decision-making.

Prediction of the evolution of the stress field of polycrystals undergoing elastic-plastic deformation with a hybrid neural network model

This work uses a neural network with convolutional layers encoding correlations in time and space to predict the evolution of the stress field given only the initial microstructure and external loading, and shows that the stress fields and their rates are in high fidelity with the crystal plasticity data and have no visible artifacts.

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