61 citations · 79 across the 6 of their papers we have counts for
6 papers
AMS-Net: Adaptive Multiscale Sparse Neural Network with Interpretable Basis Expansion for Multiphase Flow Problems
Yating Wang, Wing Tat Leung, Guang Lin
In this work, we propose an adaptive sparse learning algorithm that can be applied to learn the physical processes and obtain a sparse representation of the solution given a large…
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…
An adaptive Hessian approximated stochastic gradient MCMC method
Yating Wang, Wei Deng, Guang Lin
Bayesian approaches have been successfully integrated into training deep neural networks. One popular family is stochastic gradient Markov chain Monte Carlo methods (SG-MCMC), whic…
MFPC-Net: Multi-fidelity Physics-Constrained Neural Process
Yating Wang, Guang Lin
In this work, we propose a network which can utilize computational cheap low-fidelity data together with limited high-fidelity data to train surrogate models, where the multi-fidel…
Bayesian Sparse learning with preconditioned stochastic gradient MCMC and its applications
Yating Wang, Wei Deng, Lin Guang
In this work, we propose a Bayesian type sparse deep learning algorithm. The algorithm utilizes a set of spike-and-slab priors for the parameters in the deep neural network. The hi…
Efficient Deep Learning Techniques for Multiphase Flow Simulation in Heterogeneous Porous Media
Yating Wang, Guang Lin
We present efficient deep learning techniques for approximating flow and transport equations for both single phase and two-phase flow problems. The proposed methods take advantages…