Resolution-independent generative models based on operator learning for physics-constrained Bayesian inverse problems
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Publication:6194148
DOI10.1016/j.cma.2023.116690arXiv2308.13295OpenAlexW4389987680MaRDI QIDQ6194148
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Publication date: 19 March 2024
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2308.13295
Cites Work
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- Inverse problems as statistics
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- GAN-Based Priors for Quantifying Uncertainty in Supervised Learning
- Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations
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