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

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

31 January 2022
Wyatt Bridgman
Xiaoxuan Zhang
G. Teichert
M. Khalil
K. Garikipati
Reese E. Jones
    UQCV
    AI4CE
ArXivPDFHTML

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

3 / 3 papers shown
Title
Geometric deep learning for computational mechanics Part II: Graph
  embedding for interpretable multiscale plasticity
Geometric deep learning for computational mechanics Part II: Graph embedding for interpretable multiscale plasticity
Nikolaos N. Vlassis
WaiChing Sun
AI4CE
32
32
0
30 Jul 2022
Bayesian neural networks for weak solution of PDEs with uncertainty
  quantification
Bayesian neural networks for weak solution of PDEs with uncertainty quantification
Xiaoxuan Zhang
K. Garikipati
AI4CE
46
11
0
13 Jan 2021
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
285
9,136
0
06 Jun 2015
1