663 citations · 1.3k across the 13 of their papers we have counts for
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stat.ML2018★ 59 cited
Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations
Liu Yang, Dongkun Zhang, George Em Karniadakis
We developed a new class of physics-informed generative adversarial networks (PI-GANs) to solve in a unified manner forward, inverse and mixed stochastic problems based on a limite…
math.AP2018★ 538 cited
Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems
Dongkun Zhang, Lu Lu, Ling Guo +1
Physics-informed neural networks (PINNs) have recently emerged as an alternative way of solving partial differential equations (PDEs) without the need of building elaborate grids,…