6 citations · 6 across the 2 of their papers we have counts for
3 papers
math.NA2021★ 6 cited
Multi-variance replica exchange stochastic gradient MCMC for inverse and forward Bayesian physics-informed neural network
Guang Lin, Yating Wang, Zecheng Zhang
Physics-informed neural network (PINN) has been successfully applied in solving a variety of nonlinear non-convex forward and inverse problems. However, the training is challenging…
physics.comp-ph2021
A consistent and conservative model and its scheme for -phase--component incompressible flows
Ziyang Huang, Guang Lin, Arezoo M. Ardekani
In the present work, we propose a consistent and conservative model for multiphase and multicomponent incompressible flows, where there can be arbitrary numbers of phases and compo…
stat.AP2019
Inverse modeling of hydrologic parameters in CLM4 via generalized polynomial chaos in the Bayesian framework
Georgios Karagiannis, Zhangshuan Hou, Maoyi Huang +1
In this study, the applicability of generalized polynomial chaos (gPC) expansion for land surface model parameter estimation is evaluated. We compute the (posterior) distribution o…