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most citedWasserstein Generative Adversarial Uncertainty Quantification in Physics-Informed Neural Networks

34 citations · 49 across the 9 of their papers we have counts for

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math.NA2025

NLAFormer: Transformers Learn Numerical Linear Algebra Operations

Zhantao Ma, Yihang Gao, Michael K. Ng

Transformers are effective and efficient at modeling complex relationships and learning patterns from structured data in many applications. The main aim of this paper is to propose…

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…