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math.NA2021★ 34 cited
Wasserstein Generative Adversarial Uncertainty Quantification in Physics-Informed Neural Networks
Yihang Gao, Michael K. Ng
In this paper, we study a physics-informed algorithm for Wasserstein Generative Adversarial Networks (WGANs) for uncertainty quantification in solutions of partial differential equ…
cs.LG2021
Approximating Probability Distributions by using Wasserstein Generative Adversarial Networks
Yihang Gao, Michael K. Ng, Mingjie Zhou
Studied here are Wasserstein generative adversarial networks (WGANs) with GroupSort neural networks as their discriminators. It is shown that the error bound of the approximation f…