most citedNeuralUQ: A comprehensive library for uncertainty quantification in neural differential equations and operators

13 citations · 42 across the 7 of their papers we have counts for

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math.NA2023

Variational inference in neural functional prior using normalizing flows: Application to differential equation and operator learning problems

Xuhui Meng

Physics-informed deep learning have recently emerged as an effective tool for leveraging both observational data and available physical laws. Physics-informed neural networks (PINN…

math.NA2023★ 5 cited

Physics-informed neural networks with residual/gradient-based adaptive sampling methods for solving PDEs with sharp solutions

Zhiping Mao, Xuhui Meng

We consider solving the forward and inverse PDEs which have sharp solutions using physics-informed neural networks (PINNs) in this work. In particular, to better capture the sharpn…