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20182023
most citedPhysics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data

1.1k citations · 2.2k across the 10 of their papers we have counts for

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physics.comp-ph2019★ 1.1k cited

Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data

Yinhao Zhu, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis +1

Surrogate modeling and uncertainty quantification tasks for PDE systems are most often considered as supervised learning problems where input and output data pairs are used for tra…

physics.comp-ph2018★ 735 cited

Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification

Yinhao Zhu, Nicholas Zabaras

We are interested in the development of surrogate models for uncertainty quantification and propagation in problems governed by stochastic PDEs using a deep convolutional encoder-d…