Numerical studies of the stochastic Korteweg-de Vries equation. - Guang Lin 001; Leopold Grinberg; George E. Karniadakis (J. Comput. Phys., 2006)

Predicting shock dynamics in the presence of uncertainties. - Guang Lin 001; Chau-Hsing Su; George E. Karniadakis (J. Comput. Phys., 2006)

Stochastic Computational Fluid Mechanics. - Guang Lin 001; Xiaoliang Wan; Chau-Hsing Su; George E. Karniadakis (Comput. Sci. Eng., 2007)

Uncertainty quantification via random domain decomposition and probabilistic collocation on sparse grids. - Guang Lin 001; Alexandre M. Tartakovsky; Daniel M. Tartakovsky (J. Comput. Phys., 2010)

Numerical Studies of Three-dimensional Stochastic Darcy's Equation and Stochastic Advection-Diffusion-Dispersion Equation. - Guang Lin 001; Alexandre M. Tartakovsky (J. Sci. Comput., 2010)

MeDiCi-Cloud: A Workflow Infrastructure for Large-scale Scientific Applications. - Jian Yin 002; Guang Lin 001; Ian Gorton; Binh Han (UCC, 2011)

Point-wise hierarchical reconstruction for discontinuous Galerkin and finite volume methods for solving conservation laws. - Zhiliang Xu; Yingjie Liu; Huijing Du; Guang Lin 001; Chi-Wang Shu (J. Comput. Phys., 2011)

Adaptive ANOVA decomposition of stochastic incompressible and compressible flows. - Xiu Yang; Minseok Choi; Guang Lin 001; George E. Karniadakis (J. Comput. Phys., 2012)

Exploring Cloud Computing for Large-Scale Scientific Applications. - Guang Lin 001; Binh Han; Jian Yin 002; Ian Gorton (SERVICES, 2013)

Evaluating the impact of aquifer layer properties on geomechanical response during CO2 geological sequestration. - Jie Bao; Zhijie Xu; Guang Lin 001; Yilin Fang (Comput. Geosci., 2013)

Multi-output separable Gaussian process: Towards an efficient, fully Bayesian paradigm for uncertainty quantification. - Ilias Bilionis; Nicholas Zabaras; Bledar A. Konomi; Guang Lin 001 (J. Comput. Phys., 2013)

Numerical solution of the Stratonovich- and Ito-Euler equations: Application to the stochastic piston problem. - Zhongqiang Zhang; Xiu Yang; Guang Lin 001; George E. Karniadakis (J. Comput. Phys., 2013)

Hybrid parallel computing of minimum action method. - Xiaoliang Wan; Guang Lin 001 (Parallel Comput., 2013)

Selection of polynomial chaos bases via Bayesian model uncertainty methods with applications to sparse approximation of PDEs with stochastic inputs. - Georgios Karagiannis; Guang Lin 001 (J. Comput. Phys., 2014)

An adaptive ANOVA-based PCKF for high-dimensional nonlinear inverse modeling. - Weixuan Li; Guang Lin 001; Dongxiao Zhang (J. Comput. Phys., 2014)

Weak Galerkin finite element methods for Darcy flow: Anisotropy and heterogeneity. - Guang Lin 001; Jiangguo Liu; Lin Mu; Xiu Ye (J. Comput. Phys., 2014)

Rare-Event Simulation for the Stochastic Korteweg-de Vries Equation. - Gongjun Xu; Guang Lin 001; Jingchen Liu (SIAM/ASA J. Uncertain. Quantification, 2014)

Bayesian Treed Multivariate Gaussian Process With Adaptive Design: Application to a Carbon Capture Unit. - Bledar A. Konomi; Georgios Karagiannis; Avik Sarkar; Xin Sun 004; Guang Lin 001 (Technometrics, 2014)

Comparative Studies of Clustering Techniques for Real-Time Dynamic Model Reduction. - Emilie Hogan; Eduardo Cotilla Sanchez; Mahantesh Halappanavar; Zhenyu Huang 001; Guang Lin 001; Shuai Lu; Shaobu Wang (CoRR, 2015)

A comparative study on the weak Galerkin, discontinuous Galerkin, and mixed finite element methods. - Guang Lin 001; Jiangguo Liu; Farrah Sadre-Marandi (J. Comput. Appl. Math., 2015)

A fictitious domain method with a hybrid cell model for simulating motion of cells in fluid flow. - Wenrui Hao; Zhiliang Xu; Chun Liu 009; Guang Lin 001 (J. Comput. Phys., 2015)

A Bayesian mixed shrinkage prior procedure for spatial-stochastic basis selection and evaluation of gPC expansions: Applications to elliptic SPDEs. - Georgios Karagiannis; Bledar A. Konomi; Guang Lin 001 (J. Comput. Phys., 2015)

An adaptive importance sampling algorithm for Bayesian inversion with multimodal distributions. - Weixuan Li; Guang Lin 001 (J. Comput. Phys., 2015)

A frozen Gaussian approximation-based multi-level particle swarm optimization for seismic inversion. - Jinglai Li; Guang Lin 001; Xu Yang 018 (J. Comput. Phys., 2015)

Full scale multi-output Gaussian process emulator with nonseparable auto-covariance functions. - Bohai Zhang; Bledar A. Konomi; Huiyan Sang; Georgios Karagiannis; Guang Lin 001 (J. Comput. Phys., 2015)

Constructing Surrogate Models of Complex Systems with Enhanced Sparsity: Quantifying the Influence of Conformational Uncertainty in Biomolecular Solvation. - Huan Lei; Xiu Yang; Bin Zheng; Guang Lin 001; Nathan A. Baker (Multiscale Model. Simul., 2015)

Error analysis of finite element method for Poisson-Nernst-Planck equations. - Yuzhou Sun; Pengtao Sun; Bin Zheng; Guang Lin 001 (J. Comput. Appl. Math., 2016)

Inverse regression-based uncertainty quantification algorithms for high-dimensional models: Theory and practice. - Weixuan Li; Guang Lin 001; Bing Li (J. Comput. Phys., 2016)

Reduced basis ANOVA methods for partial differential equations with high-dimensional random inputs. - Qifeng Liao; Guang Lin 001 (J. Comput. Phys., 2016)

Gaussian process surrogates for failure detection: A Bayesian experimental design approach. - Hongqiao Wang; Guang Lin 001; Jinglai Li (J. Comput. Phys., 2016)

Enhancing sparsity of Hermite polynomial expansions by iterative rotations. - Xiu Yang; Huan Lei; Nathan A. Baker; Guang Lin 001 (J. Comput. Phys., 2016)

On Application of the Weak Galerkin Finite Element Method to a Two-Phase Model for Subsurface Flow. - Victor Ginting; Guang Lin 001; Jiangguo Liu (J. Sci. Comput., 2016)

Classification of Spatiotemporal Data via Asynchronous Sparse Sampling: Application to Flow around a Cylinder. - Ido Bright; Guang Lin 001; J. Nathan Kutz (Multiscale Model. Simul., 2016)

The stabilization of BAM neural networks with time-varying delays in the leakage terms via sampled-data control. - Li Li 046; Yongqing Yang; Guang Lin 001 (Neural Comput. Appl., 2016)

A high-order difference scheme for the fractional sub-diffusion equation. - Zhao-peng Hao; Guang Lin 001; Zhi-Zhong Sun (Int. J. Comput. Math., 2017)

A two-level stochastic collocation method for semilinear elliptic equations with random coefficients. - Luoping Chen; Bin Zheng; Guang Lin 001; Nikolaos K. Voulgarakis (J. Comput. Appl. Math., 2017)

A second-order difference scheme for the time fractional substantial diffusion equation. - Zhaopeng Hao; Wanrong Cao; Guang Lin 001 (J. Comput. Appl. Math., 2017)

On the Bayesian calibration of computer model mixtures through experimental data, and the design of predictive models. - Georgios Karagiannis; Guang Lin 001 (J. Comput. Phys., 2017)

Parallel and interacting stochastic approximation annealing algorithms for global optimisation. - Georgios Karagiannis; Bledar A. Konomi; Guang Lin 001; Faming Liang (Stat. Comput., 2017)

Comparative study of clustering techniques for real-time dynamic model reduction. - Emilie Purvine; Eduardo Cotilla Sanchez; Mahantesh Halappanavar; Zhenyu Huang 001; Guang Lin 001; Shuai Lu; Shaobu Wang (Stat. Anal. Data Min., 2017)

Visualization of Time-Varying Weather Ensembles across Multiple Resolutions. - Ayan Biswas; Guang Lin 001; Xiaotong Liu; Han-Wei Shen (IEEE Trans. Vis. Comput. Graph., 2017)

Multi-Resolution Climate Ensemble Parameter Analysis with Nested Parallel Coordinates Plots. - Junpeng Wang 001; Xiaotong Liu; Han-Wei Shen; Guang Lin 001 (IEEE Trans. Vis. Comput. Graph., 2017)

Local Feature Sufficiency Exploration for Predicting Security-Constrained Generation Dispatch in Multi-area Power Systems. - Yixuan Sun; Xiaoyuan Fan; Qiuhua Huang; Xinya Li 002; Renke Huang; Tianzhixi Yin; Guang Lin 001 (ICMLA, 2018)

CT-GAN: Conditional Transformation Generative Adversarial Network for Image Attribute Modification. - Sangpil Kim; Nick Winovich; Guang Lin 001; Karthik Ramani (CoRR, 2018)

Using automatic differentiation for compressive sensing in uncertainty quantification. - Mu Wang; Guang Lin 001; Alex Pothen (Optim. Methods Softw., 2018)

Two-Level Spectral Methods for Nonlinear Elliptic Equations with Multiple Solutions. - Yingwei Wang; Wenrui Hao; Guang Lin 001 (SIAM J. Sci. Comput., 2018)

Rotation-equivariant convolutional neural network ensembles in image processing. - Liyao Gao; Hongshan Li; Zheying Lu; Guang Lin 001 (UbiComp/ISWC Adjunct, 2019)

An Adaptive Empirical Bayesian Method for Sparse Deep Learning. - Wei Deng 002; Xiao Zhang; Faming Liang; Guang Lin 001 (NeurIPS, 2019)

Stochastic Security Assessment for Power Systems With High Renewable Energy Penetration Considering Frequency Regulation. - Yu Huang 011; Qingshan Xu 002; Sajjad Abedi; Tong Zhang; Xianqiang Jiang; Guang Lin 001 (IEEE Access, 2019)

Infrared Thermal Imaging-Based Crack Detection Using Deep Learning. - Jun Yang 056; Wei Wang 222; Guang Lin 001; Qing Li 030; Yeqing Sun; Yixuan Sun (IEEE Access, 2019)

A mixed upwind/central WENO scheme for incompressible two-phase flows. - Ziyang Huang; Guang Lin 001; Arezoo Motavalizadeh Ardekani (J. Comput. Phys., 2019)

Optimal observations-based retrieval of topography in 2D shallow water equations using PC-EnKF. - Yuepeng Wang 002; Kun Hu; Lanlan Ren; Guang Lin 001 (J. Comput. Phys., 2019)

ConvPDE-UQ: Convolutional neural networks with quantified uncertainty for heterogeneous elliptic partial differential equations on varied domains. - Nick Winovich; Karthik Ramani; Guang Lin 001 (J. Comput. Phys., 2019)

Finite Element Method for Two-Sided Fractional Differential Equations with Variable Coefficients: Galerkin Approach. - Zhaopeng Hao; Moongyu Park; Guang Lin 001; Zhiqiang Cai 001 (J. Sci. Comput., 2019)

Non-convex Learning via Replica Exchange Stochastic Gradient MCMC. - Wei Deng 002; Qi Feng 005; Liyao Gao; Faming Liang; Guang Lin 001 (ICML, 2020)

A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions. - Wei Deng 002; Guang Lin 001; Faming Liang (NeurIPS, 2020)

Machine-Learning-Based Online Transient Analysis via Iterative Computation of Generator Dynamics. - Jiaming Li; Meng Yue; Yue Zhao 007; Guang Lin 001 (SmartGridComm, 2020)

Error estimates of a spectral Petrov-Galerkin method for two-sided fractional reaction-diffusion equations. - Zhaopeng Hao; Guang Lin 001; Zhongqiang Zhang (Appl. Math. Comput., 2020)

DeepLight: Deep Lightweight Feature Interactions for Accelerating CTR Predictions in Ad Serving. - Wei Deng 002; Junwei Pan; Tian Zhou 006; Aaron Flores 001; Guang Lin 001 (CoRR, 2020)

Robust weighted SVD-type latent factor models for rating prediction. - Yiqi Gu; Xi Yang; Mengjiao Peng; Guang Lin 001 (Expert Syst. Appl., 2020)

Consistent, essentially conservative and balanced-force Phase-Field method to model incompressible two-phase flows. - Ziyang Huang; Guang Lin 001; Arezoo Motavalizadeh Ardekani (J. Comput. Phys., 2020)

Consistent and conservative scheme for incompressible two-phase flows using the conservative Allen-Cahn model. - Ziyang Huang; Guang Lin 001; Arezoo Motavalizadeh Ardekani (J. Comput. Phys., 2020)

Efficient deep learning techniques for multiphase flow simulation in heterogeneous porousc media. - Yating Wang; Guang Lin 001 (J. Comput. Phys., 2020)

Real-time computational optimal control of an MHD flow system with parameter uncertainty quantification. - Tehuan Chen; Zhigang Ren; Guang Lin 001; Zongze Wu 001; Bao-Lin Ye (J. Frankl. Inst., 2020)

A Homotopy Method with Adaptive Basis Selection for Computing Multiple Solutions of Differential Equations. - Wenrui Hao; Jan S. Hesthaven; Guang Lin 001; Bin Zheng (J. Sci. Comput., 2020)

A Low-Rank Approximated Multiscale Method for Pdes With Random Coefficients. - Na Ou; Guang Lin 001; Lijian Jiang (Multiscale Model. Simul., 2020)

Latent transformations neural network for object view synthesis. - Sangpil Kim; Nick Winovich; Hyung-Gun Chi; Guang Lin 001; Karthik Ramani (Vis. Comput., 2020)

Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction. - Wei Deng 002; Qi Feng 005; Georgios Karagiannis; Guang Lin 001; Faming Liang (ICLR, 2021)

DeepLight: Deep Lightweight Feature Interactions for Accelerating CTR Predictions in Ad Serving. - Wei Deng 002; Junwei Pan; Tian Zhou 006; Deguang Kong; Aaron Flores 001; Guang Lin 001 (WSDM, 2021)

A consistent and conservative model and its scheme for N-phase-M-component incompressible flows. - Ziyang Huang; Guang Lin 001; Arezoo Motavalizadeh Ardekani (CoRR, 2021)

Multi-variance replica exchange stochastic gradient MCMC for inverse and forward Bayesian physics-informed neural network. - Guang Lin 001; Yating Wang; Zecheng Zhang (CoRR, 2021)

HEI: hybrid explicit-implicit learning for multiscale problems. - Yalchin Efendiev; Wing Tat Leung; Guang Lin 001; Zecheng Zhang (CoRR, 2021)

PCNN: A physics-constrained neural network for multiphase flows. - Haoyang Zheng; Ziyang Huang; Guang Lin 001 (CoRR, 2021)

Theoretical and numerical studies of inverse source problem for the linear parabolic equation with sparse boundary measurements. - Guang Lin 001; Zecheng Zhang; Zhidong Zhang (CoRR, 2021)

Accelerated replica exchange stochastic gradient Langevin diffusion enhanced Bayesian DeepONet for solving noisy parametric PDEs. - Guang Lin 001; Christian Moya; Zecheng Zhang (CoRR, 2021)

Binary classification of floor vibrations for human activity detection based on dynamic mode decomposition. - Shichao Zhou; Guang Lin 001; Qinfang Qian; Chao Xu 001 (Neurocomputing, 2021)

Flow-driven spectral chaos (FSC) method for long-time integration of second-order stochastic dynamical systems. - Hugo Esquivel; Arun Prakash; Guang Lin 001 (J. Comput. Appl. Math., 2021)

A generalized multi-fidelity simulation method using sparse polynomial chaos expansion. - Jun Man; Guang Lin 001; Yijun Yao; Lingzao Zeng (J. Comput. Appl. Math., 2021)

A Comparative Study of Marginalized Graph Kernel and Message-Passing Neural Network. - Yan Xiang; Yu-Hang Tang; Guang Lin 001; Huai Sun (J. Chem. Inf. Model., 2021)

Flow-driven spectral chaos (FSC) method for simulating long-time dynamics of arbitrary-order non-linear stochastic dynamical systems. - Hugo Esquivel; Arun Prakash; Guang Lin 001 (J. Comput. Phys., 2021)

A consistent and conservative model and its scheme for N-phase-M-component incompressible flows. - Ziyang Huang; Guang Lin 001; Arezoo Motavalizadeh Ardekani (J. Comput. Phys., 2021)

Feasibility of DEIM for retrieving the initial field via dimensionality reduction. - Yuepeng Wang 002; Xuemei Ding; Kun Hu; Fangxin Fang; I. M. Navon; Guang Lin 001 (J. Comput. Phys., 2021)

Bayesian sparse learning with preconditioned stochastic gradient MCMC and its applications. - Yating Wang; Wei Deng 002; Guang Lin 001 (J. Comput. Phys., 2021)

An adaptive Hessian approximated stochastic gradient MCMC method. - Yating Wang; Wei Deng 002; Guang Lin 001 (J. Comput. Phys., 2021)

SubTSBR to tackle high noise and outliers for data-driven discovery of differential equations. - Sheng Zhang; Guang Lin 001 (J. Comput. Phys., 2021)

Identifiability and predictability of integer- and fractional-order epidemiological models using physics-informed neural networks. - Ehsan Kharazmi; Min Cai; Xiaoning Zheng; Zhen Zhang 029; Guang Lin 001; George Em Karniadakis (Nat. Comput. Sci., 2021)

An integrated framework for building trustworthy data-driven epidemiological models: Application to the COVID-19 outbreak in New York City. - Sheng Zhang; Joan Ponce; Zhen Zhang 029; Guang Lin 001; George E. Karniadakis (PLoS Comput. Biol., 2021)

Gaussian Process Assisted Active Learning of Physical Laws. - Jiuhai Chen; Lulu Kang; Guang Lin 001 (Technometrics, 2021)

Post-Fault Power Grid Voltage Prediction via 1D-CNN with Spatial Coupling. - Carson Hu; Guang Lin 001; Bao Wang 001; Meng Yue; Jack Xin (AI4I, 2022)

Glassoformer: A Query-Sparse Transformer for Post-Fault Power Grid Voltage Prediction. - Yunling Zheng; Carson Hu; Guang Lin 001; Meng Yue; Bao Wang 001; Jack Xin (ICASSP, 2022)

Interacting Contour Stochastic Gradient Langevin Dynamics. - Wei Deng 002; Siqi Liang 005; Botao Hao; Guang Lin 001; Faming Liang (ICLR, 2022)

Fed-DeepONet: Stochastic Gradient-Based Federated Training of Deep Operator Networks. - Christian Moya; Guang Lin 001 (Algorithms, 2022)

HomPINNs: Homotopy physics-informed neural networks for learning multiple solutions of nonlinear elliptic differential equations. - Yao Huang; Wenrui Hao; Guang Lin 001 (Comput. Math. Appl., 2022)

Inverse Modeling of Hydrologic Parameters in CLM4 via Generalized Polynomial Chaos in the Bayesian Framework. - Georgios Karagiannis; Zhangshuan Hou; Maoyi Huang; Guang Lin 001 (Comput., 2022)

PAGP: A physics-assisted Gaussian process framework with active learning for forward and inverse problems of partial differential equations. - Jiahao Zhang 002; Shiqi Zhang 006; Guang Lin 001 (CoRR, 2022)

MultiAuto-DeepONet: A Multi-resolution Autoencoder DeepONet for Nonlinear Dimension Reduction, Uncertainty Quantification and Operator Learning of Forward and Inverse Stochastic Problems. - Jiahao Zhang 002; Shiqi Zhang 006; Guang Lin 001 (CoRR, 2022)

RMFGP: Rotated Multi-fidelity Gaussian process with Dimension Reduction for High-dimensional Uncertainty Quantification. - Jiahao Zhang 002; Shiqi Zhang 006; Guang Lin 001 (CoRR, 2022)

2-d signature of images and texture classification. - Sheng Zhang; Guang Lin 001; Samy Tindel (CoRR, 2022)

On Learning the Dynamical Response of Nonlinear Control Systems with Deep Operator Networks. - Guang Lin 001; Christian Moya; Zecheng Zhang (CoRR, 2022)

Efficient Chemical Space Exploration Using Active Learning Based on Marginalized Graph Kernel: an Application for Predicting the Thermodynamic Properties of Alkanes with Molecular Simulation. - Yan Xiang; Yu-Hang Tang; Zheng Gong; Hongyi Liu; Liang Wu; Guang Lin 001; Huai Sun (CoRR, 2022)

A replica exchange preconditioned Crank-Nicolson Langevin dynamic MCMC method for Bayesian inverse problems. - Na Ou; Zecheng Zhang; Guang Lin 001 (CoRR, 2022)

Feature Selection Techniques for a Machine Learning Model to Detect Autonomic Dysreflexia. - Shruthi Suresh; David T. Newton; Thomas H. Everett; Guang Lin 001; Bradley S. Duerstock (Frontiers Neuroinformatics, 2022)

Vapor-liquid equilibrium estimation of n-alkane/nitrogen mixtures using neural networks. - Suman Chakraborty; Yixuan Sun; Guang Lin 001; Li Qiao (J. Comput. Appl. Math., 2022)

A consistent and conservative Phase-Field method for multiphase incompressible flows. - Ziyang Huang; Guang Lin 001; Arezoo Motavalizadeh Ardekani (J. Comput. Appl. Math., 2022)

Block triangular preconditioning for stochastic Galerkin method. - Dongwu Wang; Bin Zheng; Long Chen 002; Guang Lin 001; Jinchao Xu (J. Comput. Appl. Math., 2022)

Efficient hybrid explicit-implicit learning for multiscale problems. - Yalchin Efendiev; Wing Tat Leung; Guang Lin 001; Zecheng Zhang (J. Comput. Phys., 2022)

Multi-element flow-driven spectral chaos (ME-FSC) method for uncertainty quantification of dynamical systems. - Hugo Esquivel; Arun Prakash; Guang Lin 001 (J. Comput. Phys., 2022)

RotEqNet: Rotation-equivariant network for fluid systems with symmetric high-order tensors. - Liyao Gao; Yifan Du; Hongshan Li; Guang Lin 001 (J. Comput. Phys., 2022)

A consistent and conservative Phase-Field model for thermo-gas-liquid-solid flows including liquid-solid phase change. - Ziyang Huang; Guang Lin 001; Arezoo Motavalizadeh Ardekani (J. Comput. Phys., 2022)

Implementing contact angle boundary conditions for second-order Phase-Field models of wall-bounded multiphase flows. - Ziyang Huang; Guang Lin 001; Arezoo Motavalizadeh Ardekani (J. Comput. Phys., 2022)

NH-PINN: Neural homogenization-based physics-informed neural network for multiscale problems. - Wing Tat Leung; Guang Lin 001; Zecheng Zhang (J. Comput. Phys., 2022)

Multi-variance replica exchange SGMCMC for inverse and forward problems via Bayesian PINN. - Guang Lin 001; Yating Wang; Zecheng Zhang (J. Comput. Phys., 2022)

Data-driven causal model discovery and personalized prediction in Alzheimer's disease. - Haoyang Zheng; Jeffrey R. Petrella; P. Murali Doraiswamy; Guang Lin 001; Wenrui Hao (npj Digit. Medicine, 2022)

An adaptively weighted stochastic gradient MCMC algorithm for Monte Carlo simulation and global optimization. - Wei Deng 002; Guang Lin 001; Faming Liang (Stat. Comput., 2022)

Learning-PDE-Based Approximate Optimal Control for an MHD System With Uncertainty Quantification. - Tehuan Chen; Zhigang Ren; Guang Lin 001; Chao Xu 001 (IEEE Trans. Syst. Man Cybern. Syst., 2022)

AMS-Net: Adaptive Multiscale Sparse Neural Network with Interpretable Basis Expansion for Multiphase Flow Problems. - Yating Wang; Wing Tat Leung; Guang Lin 001 (Multiscale Model. Simul., 2022)

Non-reversible Parallel Tempering for Deep Posterior Approximation. - Wei Deng 002; Qian Zhang 067; Qi Feng 005; Faming Liang; Guang Lin 001 (AAAI, 2023)

Fast Replica Exchange Stochastic Gradient Langevin Dynamics. - Guanxun Li; Guang Lin 001; Zecheng Zhang; Quan Zhou (CoRR, 2023)

On Approximating the Dynamic Response of Synchronous Generators via Operator Learning: A Step Towards Building Deep Operator-based Power Grid Simulators. - Christian Moya; Guang Lin 001; Tianqiao Zhao; Meng Yue (CoRR, 2023)

Numerical Stability for Differential Equations with Memory. - Guihong Wang; Yuqing Li; Tao Luo; Zheng Ma; Nung Kwan Yip; Guang Lin 001 (CoRR, 2023)

Deep Operator Learning-based Surrogate Models with Uncertainty Quantification for Optimizing Internal Cooling Channel Rib Profiles. - Izzet Sahin; Christian Moya; Amirhossein Mollaali; Guang Lin 001; Guillermo Paniagua (CoRR, 2023)

An Element-wise RSAV Algorithm for Unconstrained Optimization Problems. - Shiheng Zhang; Jiahao Zhang 002; Jie Shen 001; Guang Lin 001 (CoRR, 2023)

Restoring the Discontinuous Heat Equation Source Using Sparse Boundary Data and Dynamic Sensors. - Guang Lin 001; Na Ou; Zecheng Zhang; Zhidong Zhang (CoRR, 2023)

Backdiff: a diffusion model for generalized transferable protein backmapping. - Yikai Liu; Ming Chen; Guang Lin 001 (CoRR, 2023)

D2NO: Efficient Handling of Heterogeneous Input Function Spaces with Distributed Deep Neural Operators. - Zecheng Zhang; Christian Moya; Lu Lu; Guang Lin 001; Hayden Schaeffer (CoRR, 2023)

A Physics-Guided Bi-Fidelity Fourier-Featured Operator Learning Framework for Predicting Time Evolution of Drag and Lift Coefficients. - Amirhossein Mollaali; Izzet Sahin; Iqrar Raza; Christian Moya; Guillermo Paniagua; Guang Lin 001 (CoRR, 2023)

Fair Supervised Learning with A Simple Random Sampler of Sensitive Attributes. - Jinwon Sohn; Qifan Song; Guang Lin 001 (CoRR, 2023)

B-LSTM-MIONet: Bayesian LSTM-based Neural Operators for Learning the Response of Complex Dynamical Systems to Length-Variant Multiple Input Functions. - Zhihao Kong; Amirhossein Mollaali; Christian Moya; Na Lu; Guang Lin 001 (CoRR, 2023)

Unbiasing Enhanced Sampling on a High-dimensional Free Energy Surface with Deep Generative Model. - Yikai Liu; Tushar K. Ghosh; Guang Lin 001; Ming Chen (CoRR, 2023)

Learning the dynamical response of nonlinear non-autonomous dynamical systems with deep operator neural networks. - Guang Lin 001; Christian Moya; Zecheng Zhang (Eng. Appl. Artif. Intell., 2023)

DeepONet-grid-UQ: A trustworthy deep operator framework for predicting the power grid's post-fault trajectories. - Christian Moya; Shiqi Zhang 006; Guang Lin 001; Meng Yue (Neurocomputing, 2023)

B-DeepONet: An enhanced Bayesian DeepONet for solving noisy parametric PDEs using accelerated replica exchange SGLD. - Guang Lin 001; Christian Moya; Zecheng Zhang (J. Comput. Phys., 2023)

DAE-PINN: a physics-informed neural network model for simulating differential algebraic equations with application to power networks. - Christian Moya; Guang Lin 001 (Neural Comput. Appl., 2023)

NSGA-PINN: A Multi-Objective Optimization Method for Physics-Informed Neural Network Training. - Binghang Lu; Christian Moya; Guang Lin 001 (Algorithms, 2023)

Interpretable Molecular Property Predictions Using Marginalized Graph Kernels. - Yan Xiang; Yu-Hang Tang; Guang Lin 001; Daniel Reker (J. Chem. Inf. Model., 2023)

DeepGraphONet: A Deep Graph Operator Network to Learn and Zero-Shot Transfer the Dynamic Response of Networked Systems. - Yixuan Sun; Christian Moya; Guang Lin 001; Meng Yue (IEEE Syst. J., 2023)

Statistical Learning for Nonlinear Dynamical Systems with Applications to Aircraft-UAV Collisions. - Xinchao Liu; Xiao Liu; Tulin Kaman; Xiaohua Lu; Guang Lin 001 (Technometrics, 2023)

Efficient Exploration of Chemical Compound Space Using Active Learning for Prediction of Thermodynamic Properties of Alkane Molecules. - Yan Xiang; Yu-Hang Tang; Zheng Gong; Hongyi Liu; Liang Wu; Guang Lin 001; Huai Sun (J. Chem. Inf. Model., 2023)

Bayesian, Multifidelity Operator Learning for Complex Engineering Systems-A Position Paper. - Christian Moya; Guang Lin 001 (J. Comput. Inf. Sci. Eng., 2023)

Federated X-armed Bandit. - Wenjie Li; Qifan Song; Jean Honorio; Guang Lin 001 (AAAI, 2024)

Accelerating Approximate Thompson Sampling with Underdamped Langevin Monte Carlo. - Haoyang Zheng; Wei Deng 002; Christian Moya; Guang Lin 001 (AISTATS, 2024)

Constrained Exploration via Reflected Replica Exchange Stochastic Gradient Langevin Dynamics. - Haoyang Zheng; Hengrong Du; Qi Feng 005; Wei Deng 002; Guang Lin 001 (ICML, 2024)

On Convergence of Federated Averaging Langevin Dynamics. - Wei Deng 002; Qian Zhang 067; Yian Ma; Zhao Song 002; Guang Lin 001 (UAI, 2024)

Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks. - Christian Moya; Amirhossein Mollaali; Zecheng Zhang; Lu Lu; Guang Lin 001 (CoRR, 2024)

Adversarial Vulnerability as a Consequence of On-Manifold Inseparibility. - Rajdeep Haldar; Yue Xing 002; Qifan Song; Guang Lin 001 (CoRR, 2024)

Where to Build Food Banks and Pantries: A Two-Level Machine Learning Approach. - Gavin Ruan; Ziqi Guo; Guang Lin 001 (CoRR, 2024)

LES-SINDy: Laplace-Enhanced Sparse Identification of Nonlinear Dynamical Systems. - Haoyang Zheng; Guang Lin 001 (CoRR, 2024)

An Energy-Based Self-Adaptive Learning Rate for Stochastic Gradient Descent: Enhancing Unconstrained Optimization with VAV method. - Jiahao Zhang 002; Christian Moya; Guang Lin 001 (CoRR, 2024)

DeepONet as a Multi-Operator Extrapolation Model: Distributed Pretraining with Physics-Informed Fine-Tuning. - Zecheng Zhang; Christian Moya; Lu Lu; Guang Lin 001; Hayden Schaeffer (CoRR, 2024)

Loss Terms and Operator Forms of Koopman Autoencoders. - Dustin Enyeart; Guang Lin 001 (CoRR, 2024)

Some Best Practices in Operator Learning. - Dustin Enyeart; Guang Lin 001 (CoRR, 2024)

Adversarial Autoencoders in Operator Learning. - Dustin Enyeart; Guang Lin 001 (CoRR, 2024)

A replica exchange preconditioned Crank-Nicolson Langevin dynamic MCMC method with multi-variance strategy for Bayesian inverse problems. - Na Ou; Zecheng Zhang; Guang Lin 001 (J. Comput. Phys., 2024)

Bayesian Deep Operator Learning for Homogenized to Fine-Scale Maps for Multiscale PDE. - Zecheng Zhang; Christian Moya; Wing Tat Leung; Guang Lin 001; Hayden Schaeffer (Multiscale Model. Simul., 2024)

Accelerating inverse inference of ensemble Kalman filter via reduced-order model trained using adaptive sparse observations. - Yuepeng Wang 002; Jie Li; Wenju Zhao; I. M. Navon; Guang Lin 001 (J. Comput. Phys., 2024)

Energy-dissipative evolutionary deep operator neural networks. - Jiahao Zhang 002; Shiheng Zhang; Jie Shen 001; Guang Lin 001 (J. Comput. Phys., 2024)

HomPINNs: Homotopy physics-informed neural networks for solving the inverse problems of nonlinear differential equations with multiple solutions. - Haoyang Zheng; Yao Huang; Ziyang Huang; Wenrui Hao; Guang Lin 001 (J. Comput. Phys., 2024)

Bayesian Federated Learning with Hamiltonian Monte Carlo: Algorithm and Theory. - Jiajun Liang; Qian Zhang 067; Wei Deng 002; Qifan Song; Guang Lin 001 (J. Comput. Graph. Stat., 2025)

FPINN-deeponet: A physics-informed operator learning framework for multi-term time-fractional mixed diffusion-wave equations. - Binghang Lu; Zhaopeng Hao; Christian Moya; Guang Lin 001 (J. Comput. Phys., 2025)

Conformalized prediction of post-fault voltage trajectories using pre-trained and finetuned attention-driven neural operators. - Amirhossein Mollaali; Gabriel Zufferey; Gonzalo Constante-Flores; Christian Moya; Can Li; Meng Yue; Guang Lin 001 (Neural Networks, 2025)

A Relaxed Vector Auxiliary Variable Algorithm for Unconstrained Optimization Problems. - Shiheng Zhang; Jiahao Zhang 002; Jie Shen 001; Guang Lin 001 (SIAM J. Sci. Comput., 2025)

Exploring Non-Convex Discrete Energy Landscapes: A Langevin-Like Sampler with Replica Exchange. - Haoyang Zheng; Ruqi Zhang; Guang Lin 001 (CoRR, 2025)

Muti-Fidelity Prediction and Uncertainty Quantification with Laplace Neural Operators for Parametric Partial Differential Equations. - Haoyang Zheng; Guang Lin 001 (CoRR, 2025)

LLM Safety Alignment is Divergence Estimation in Disguise. - Rajdeep Haldar; Ziyi Wang; Qifan Song; Guang Lin 001; Yue Xing 002 (CoRR, 2025)

Energy-Dissipative Evolutionary Kolmogorov-Arnold Networks for Complex PDE Systems. - Guang Lin 001; Changhong Mou; Jiahao Zhang 002 (CoRR, 2025)

LAPD: Langevin-Assisted Bayesian Active Learning for Physical Discovery. - Cindy Xiangrui Kong; Haoyang Zheng; Guang Lin 001 (CoRR, 2025)

Coefficient-to-Basis Network: A Fine-Tunable Operator Learning Framework for Inverse Problems with Adaptive Discretizations and Theoretical Guarantees. - Zecheng Zhang; Hao Liu; Wenjing Liao; Guang Lin 001 (CoRR, 2025)

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning. - Amirhossein Mollaali; Christian B. Moya; Amanda A. Howard; Alexander Heinlein; Panos Stinis; Guang Lin 001 (CoRR, 2025)

Linear Spatial World Models Emerge in Large Language Models. - Matthieu Tehenan; Christian B. Moya; Tenghai Long; Guang Lin 001 (CoRR, 2025)