2.5k citations · 6.9k across the 23 of their papers we have counts for
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A fast multi-fidelity method with uncertainty quantification for complex data correlations: Application to vortex-induced vibrations of marine risers
Xuhui Meng, Zhicheng Wang, Dixia Fan +2
We develop a fast multi-fidelity modeling method for very complex correlations between high- and low-fidelity data by working in modal space to extract the proper correlation funct…
Multi-fidelity Bayesian Neural Networks: Algorithms and Applications
Xuhui Meng, Hessam Babaee, George Em Karniadakis
We propose a new class of Bayesian neural networks (BNNs) that can be trained using noisy data of variable fidelity, and we apply them to learn function approximations as well as t…
Physics-informed neural networks for solving forward and inverse flow problems via the Boltzmann-BGK formulation
Qin Lou, Xuhui Meng, George Em Karniadakis
In this study, we employ physics-informed neural networks (PINNs) to solve forward and inverse problems via the Boltzmann-BGK formulation (PINN-BGK), enabling PINNs to model flows…
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data
Liu Yang, Xuhui Meng, George Em Karniadakis
We propose a Bayesian physics-informed neural network (B-PINN) to solve both forward and inverse nonlinear problems described by partial differential equations (PDEs) and noisy dat…