most citedIB-UQ: Information bottleneck based uncertainty quantification for neural function regression and neural operator learning

2 citations · 2 across the 2 of their papers we have counts for

collaborators

2 papers

math.NA2023

Bi-orthogonal fPINN: A physics-informed neural network method for solving time-dependent stochastic fractional PDEs

Lei Ma, Rong xin Li, Fanhai Zeng +2

Fractional partial differential equations (FPDEs) can effectively represent anomalous transport and nonlocal interactions. However, inherent uncertainties arise naturally in real a…

math.NA2023★ 2 cited

IB-UQ: Information bottleneck based uncertainty quantification for neural function regression and neural operator learning

Ling Guo, Hao Wu, Wenwen Zhou +2

We propose a novel framework for uncertainty quantification via information bottleneck (IB-UQ) for scientific machine learning tasks, including deep neural network (DNN) regression…