6 citations · 12 across the 7 of their papers we have counts for
18 papers
Spatial Damage Characterization in Self-Sensing Materials via Neural Network-Aided Electrical Impedance Tomography: A Computational Study
Lang Zhao, Tyler Tallman, Guang Lin
Continuous structural health monitoring (SHM) and integrated nondestructive evaluation (NDE) are important for ensuring the safe operation of high-risk engineering structures. Rece…
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…
Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction
Wei Deng, Qi Feng, Georgios Karagiannis +2
Replica exchange stochastic gradient Langevin dynamics (reSGLD) has shown promise in accelerating the convergence in non-convex learning; however, an excessively large correction f…
Multifidelity Data Fusion via Gradient-Enhanced Gaussian Process Regression
Yixiang Deng, Guang Lin, Xiu Yang
We propose a data fusion method based on multi-fidelity Gaussian process regression (GPR) framework. This method combines available data of the quantity of interest (QoI) and its g…
Non-convex Learning via Replica Exchange Stochastic Gradient MCMC
Wei Deng, Qi Feng, Liyao Gao +2
Replica exchange Monte Carlo (reMC), also known as parallel tempering, is an important technique for accelerating the convergence of the conventional Markov Chain Monte Carlo (MCMC…