61 citations · 285 across the 30 of their papers we have counts for
30 papers
A Bayesian Framework for Built-in Input Dimension Reduction for Gaussian Process Modeling
Eric Herrison Gyamfi, Emily L. Kang, Bledar A. Konomi +1
Gaussian process (GP) modeling is widely used in computational science and engineering. However, fitting a GP to high-dimensional inputs remains challenging due to the curse of dim…
Bayesian data-driven discovery of partial differential equations with variable coefficients
Aoxue Chen, Yifan Du, Liyao Mars Gao +1
The discovery of Partial Differential Equations (PDEs) is an essential task for applied science and engineering. However, data-driven discovery of PDEs is generally challenging, pr…
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…
A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions
Wei Deng, Guang Lin, Faming Liang
We propose an adaptively weighted stochastic gradient Langevin dynamics algorithm (SGLD), so-called contour stochastic gradient Langevin dynamics (CSGLD), for Bayesian learning in…
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…
Augmented Gaussian Random Field: Theory and Computation
Sheng Zhang, Xiu Yang, Samy Tindel +1
We propose the novel augmented Gaussian random field (AGRF), which is a universal framework incorporating the data of observable and derivatives of any order. Rigorous theory is es…
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…
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…
Peri-Net-Pro: The neural processes with quantified uncertainty for crack patterns
Moonseop Kim, Guang Lin
This paper uses the peridynamic theory, which is well-suited to crack studies, to predict the crack patterns in a moving disk and classify them according to the modes and finally p…
Multi-Fidelity Gaussian Process based Empirical Potential Development for Si:H Nanowires
Moonseop Kim, Huayi Yin, Guang Lin
In material modeling, the calculation speed using the empirical potentials is fast compared to the first principle calculations, but the results are not as accurate as of the first…
RotEqNet: Rotation-Equivariant Network for Fluid Systems with Symmetric High-Order Tensors
Liyao Gao, Yifan Du, Hongshan Li +1
In the recent application of scientific modeling, machine learning models are largely applied to facilitate computational simulations of fluid systems. Rotation symmetry is a gener…
DeepLight: Deep Lightweight Feature Interactions for Accelerating CTR Predictions in Ad Serving
Wei Deng, Junwei Pan, Tian Zhou +3
Click-through rate (CTR) prediction is a crucial task in online display advertising. The embedding-based neural networks have been proposed to learn both explicit feature interacti…
An Adaptive Empirical Bayesian Method for Sparse Deep Learning
Wei Deng, Xiao Zhang, Faming Liang +1
We propose a novel adaptive empirical Bayesian method for sparse deep learning, where the sparsity is ensured via a class of self-adaptive spike-and-slab priors. The proposed metho…
Gaussian Process Assisted Active Learning of Physical Laws
Jiuhai Chen, Lulu Kang, Guang Lin
In many areas of science and engineering, discovering the governing differential equations from the noisy experimental data is an essential challenge. It is also a critical step in…
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…
SubTSBR to tackle high noise and outliers for data-driven discovery of differential equations
Sheng Zhang, Guang Lin
Data-driven discovery of differential equations has been an emerging research topic. We propose a novel algorithm subsampling-based threshold sparse Bayesian regression (SubTSBR) t…
Bayesian inverse regression for dimension reduction with small datasets
Xin Cai, Guang Lin, Jinglai Li
We consider supervised dimension reduction problems, namely to identify a low dimensional projection of the predictors $\-x$ which can retain the statistical relationship between $…
On the anticipative nonlinear filtering problem and its stability
Guang Lin, Yanghui Liu, Samy Tindel
In this paper, we consider an anticipative nonlinear filtering problem, in which the observation noise is correlated with the past of the signal. This new signal-observation model…
Trimmed Ensemble Kalman Filter for Nonlinear and Non-Gaussian Data Assimilation Problems
Weixuan Li, W. Steven Rosenthal, Guang Lin
We study the ensemble Kalman filter (EnKF) algorithm for sequential data assimilation in a general situation, that is, for nonlinear forecast and measurement models with non-additi…
Turbulence Generation from a stochastic wavelet model
Yifan Du, Guang Lin
This research presents a new turbulence generation method based on stochastic wavelets and tests its various properties in both homogeneous and inhomogeneous turbulence. Turbulence…
On the Bayesian calibration of expensive computer models with input dependent parameters
Georgios Karagiannis, Bledar A. Konomi, Guang Lin
Computer models, aiming at simulating a complex real system, are often calibrated in the light of data to improve performance. Standard calibration methods assume that the optimal…