b'Guang Lin'

research

∙ 05/11/2023

Numerical Stability for Differential Equations with Memory

In this work, we systematically investigate linear multi-step methods fo...

0 Guihong Wang, et al. ∙

research

∙ 04/06/2023

HomPINNs: homotopy physics-informed neural networks for solving the inverse problems of nonlinear differential equations with multiple solutions

Due to the complex behavior arising from non-uniqueness, symmetry, and b...

0 Haoyang Zheng, et al. ∙

research

∙ 03/03/2023

NSGA-PINN: A Multi-Objective Optimization Method for Physics-Informed Neural Network Training

This paper presents NSGA-PINN, a multi-objective optimization framework ...

0 Binghang Lu, et al. ∙

research

∙ 01/29/2023

On Approximating the Dynamic Response of Synchronous Generators via Operator Learning: A Step Towards Building Deep Operator-based Power Grid Simulators

This paper designs an Operator Learning framework to approximate the dyn...

0 Christian Moya, et al. ∙

research

∙ 11/20/2022

Non-reversible Parallel Tempering for Deep Posterior Approximation

Parallel tempering (PT), also known as replica exchange, is the go-to wo...

0 Wei Deng, et al. ∙

research

∙ 09/21/2022

DeepGraphONet: A Deep Graph Operator Network to Learn and Zero-shot Transfer the Dynamic Response of Networked Systems

This paper develops a Deep Graph Operator Network (DeepGraphONet) framew...

0 Yixuan Sun, et al. ∙

research

∙ 09/01/2022

Efficient Chemical Space Exploration Using Active Learning Based on Marginalized Graph Kernel: an Application for Predicting the Thermodynamic Properties of Alkanes with Molecu

We introduce an explorative active learning (AL) algorithm based on Gaus...

0 Yan Xiang, et al. ∙

research

∙ 07/24/2022

AMS-Net: Adaptive Multiscale Sparse Neural Network with Interpretable Basis Expansion for Multiphase Flow Problems

In this work, we propose an adaptive sparse learning algorithm that can ...

0 Yating Wang, et al. ∙

research

∙ 07/21/2022

Multi-element flow-driven spectral chaos (ME-FSC) method for uncertainty quantification of dynamical systems

The flow-driven spectral chaos (FSC) is a recently developed method for ...

0 Hugo Esquivel, et al. ∙

research

∙ 05/30/2022

Federated X-Armed Bandit

This work establishes the first framework of federated 𝒳-armed bandit, w...

0 Wenjie Li, et al. ∙

research

∙ 04/11/2022

RMFGP: Rotated Multi-fidelity Gaussian process with Dimension Reduction for High-dimensional Uncertainty Quantification

Multi-fidelity modelling arises in many situations in computational scie...

0 Jiahao Zhang, et al. ∙

research

∙ 04/07/2022

MultiAuto-DeepONet: A Multi-resolution Autoencoder DeepONet for Nonlinear Dimension Reduction, Uncertainty Quantification and Operator Learning of Forward and Inverse Stochasti

A new data-driven method for operator learning of stochastic differentia...

0 Jiahao Zhang, et al. ∙

research

∙ 04/06/2022

PAGP: A physics-assisted Gaussian process framework with active learning for forward and inverse problems of partial differential equations

In this work, a Gaussian process regression(GPR) model incorporated with...

0 Jiahao Zhang, et al. ∙

research

∙ 02/27/2022

Federated Online Sparse Decision Making

This paper presents a novel federated linear contextual bandits model, w...

0 Chi-Hua Wang, et al. ∙

research

∙ 02/20/2022

Interacting Contour Stochastic Gradient Langevin Dynamics

We propose an interacting contour stochastic gradient Langevin dynamics ...

0 Wei Deng, et al. ∙

research

∙ 02/15/2022

DeepONet-Grid-UQ: A Trustworthy Deep Operator Framework for Predicting the Power Grid's Post-Fault Trajectories

This paper proposes a new data-driven method for the reliable prediction...

0 Christian Moya, et al. ∙

research

∙ 01/22/2022

glassoformer: a query-sparse transformer for post-fault power grid voltage prediction

We propose GLassoformer, a novel and efficient transformer architecture ...

0 Yunling Zheng, et al. ∙

research

∙ 12/09/2021

On Convergence of Federated Averaging Langevin Dynamics

We propose a federated averaging Langevin algorithm (FA-LD) for uncertai...

6 Wei Deng, et al. ∙

research

∙ 11/22/2021

Deformation Robust Roto-Scale-Translation Equivariant CNNs

Incorporating group symmetry directly into the learning process has prov...

0 Liyao Gao, et al. ∙

research

∙ 09/18/2021

PCNN: A physics-constrained neural network for multiphase flows

The present study develops a physics-constrained neural network (PCNN) t...

13 Haoyang Zheng, et al. ∙

research

∙ 09/09/2021

DAE-PINN: A Physics-Informed Neural Network Model for Simulating Differential-Algebraic Equations with Application to Power Networks

Deep learning-based surrogate modeling is becoming a promising approach ...

0 Christian Moya, et al. ∙

research

∙ 05/21/2021

Flow-driven spectral chaos (FSC) method for long-time integration of second-order stochastic dynamical systems

For decades, uncertainty quantification techniques based on the spectral...

0 Hugo Esquivel, et al. ∙

research

∙ 03/14/2021

Implementing contact angle boundary conditions for second-order Phase-Field models of wall-bounded multiphase flows

In the present work, a general formulation is proposed to implement the ...

0 Ziyang Huang, et al. ∙

research

∙ 02/13/2021

A consistent and conservative Phase-Field model for thermo-gas-liquid-solid flows including liquid-solid phase change

In the present study, a consistent and conservative Phase-Field model is...

0 Ziyang Huang, et al. ∙

research

∙ 02/02/2021

Robust data-driven discovery of partial differential equations with time-dependent coefficients

In this work, we propose a robust Bayesian sparse learning algorithm bas...

0 Aoxue Chen, et al. ∙

research

∙ 01/12/2021

A consistent and conservative model and its scheme for N-phase-M-component incompressible flows

In the present work, we propose a consistent and conservative model for ...

0 Ziyang Huang, et al. ∙

research

∙ 12/02/2020

Flow-driven spectral chaos (FSC) method for simulating long-time dynamics of arbitrary-order non-linear stochastic dynamical systems

Uncertainty quantification techniques such as the time-dependent general...

0 Hugo Esquivel, et al. ∙

research

∙ 11/03/2020

Single Shot Reversible GAN for BCG artifact removal in simultaneous EEG-fMRI

Simultaneous EEG-fMRI acquisition and analysis technology has been widel...

0 Guang Lin, et al. ∙

research

∙ 10/19/2020

A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions

We propose an adaptively weighted stochastic gradient Langevin dynamics ...

0 Wei Deng, et al. ∙

research

∙ 10/07/2020

Predicting Mechanical Properties from Microstructure Images in Fiber-reinforced Polymers using Convolutional Neural Networks

Evaluating the mechanical response of fiber-reinforced composites can be...

0 Yixuan Sun, et al. ∙

research

∙ 10/04/2020

Spatial Damage Characterization in Self-Sensing Materials via Neural Network-Aided Electrical Impedance Tomography: A Computational Study

Continuous structural health monitoring (SHM) and integrated nondestruct...

0 Lang Zhao, et al. ∙

research

∙ 10/03/2020

An adaptive Hessian approximated stochastic gradient MCMC method

Bayesian approaches have been successfully integrated into training deep...

0 Yating Wang, et al. ∙

research

∙ 10/03/2020

MFPC-Net: Multi-fidelity Physics-Constrained Neural Process

In this work, we propose a network which can utilize computational cheap...

0 Yating Wang, et al. ∙

research

∙ 10/02/2020

Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction

Replica exchange stochastic gradient Langevin dynamics (reSGLD) has show...

8 Wei Deng, et al. ∙

research

∙ 09/03/2020

Augmented Gaussian Random Field: Theory and Computation

We provide a rigorous theoretical foundation for incorporating data of o...

0 Sheng Zhang, et al. ∙

research

∙ 08/12/2020

Non-convex Learning via Replica Exchange Stochastic Gradient MCMC

Replica exchange Monte Carlo (reMC), also known as parallel tempering, i...

12 Wei Deng, et al. ∙

research

∙ 08/03/2020

Multifidelity Data Fusion via Gradient-Enhanced Gaussian Process Regression

We propose a data fusion method based on multi-fidelity Gaussian process...

0 Yixiang Deng, et al. ∙

research

∙ 05/23/2020

Peri-Net-Pro: The neural processes with quantified uncertainty for crack patterns

This paper uses the peridynamic theory, which is well-suited to crack st...

14 Moonseop Kim, et al. ∙

research

∙ 05/11/2020

Multi-Fidelity Gaussian Process based Empirical Potential Development for Si:H Nanowires

In material modeling, the calculation speed using the empirical potentia...

0 Moonseop Kim, et al. ∙

research

∙ 04/28/2020

RotEqNet: Rotation-Equivariant Network for Fluid Systems with Symmetric High-Order Tensors

In the recent application of scientific modeling, machine learning model...

0 Liyao Gao, et al. ∙

research

∙ 02/17/2020

A Sparse Deep Factorization Machine for Efficient CTR prediction

Click-through rate (CTR) prediction is a crucial task in online display ...

4 Wei Deng, et al. ∙

research

∙ 10/23/2019

An Adaptive Empirical Bayesian Method for Sparse Deep Learning

We propose a novel adaptive empirical Bayesian (AEB) method for sparse d...

0 Wei Deng, et al. ∙

research

∙ 10/18/2019

Inverse modeling of hydrologic parameters in CLM4 via generalized polynomial chaos in the Bayesian framework

In this study, the applicability of generalized polynomial chaos (gPC) e...

0 Georgios Karagiannis, et al. ∙

research

∙ 10/07/2019

Gaussian Process Assisted Active Learning of Physical Laws

In many areas of science and engineering, discovering the governing diff...

0 Jiuhai Chen, et al. ∙

research

∙ 07/22/2019

Efficient Deep Learning Techniques for Multiphase Flow Simulation in Heterogeneous Porous Media

We present efficient deep learning techniques for approximating flow and...

0 Yating Wang, et al. ∙

research

∙ 07/17/2019

Robust subsampling-based sparse Bayesian inference to tackle four challenges (large noise, outliers, data integration, and extrapolation) in the discovery of physical laws from

The derivation of physical laws is a dominant topic in scientific resear...

23 Sheng Zhang, et al. ∙

research

∙ 07/17/2019

Robust data-driven discovery of governing physical laws using a new subsampling-based sparse Bayesian method to tackle four challenges (large noise, outliers, data integration,

The derivation of physical laws is a dominant topic in scientific resear...

6 Sheng Zhang, et al. ∙

research

∙ 06/19/2019

Bayesian inverse regression for supervised dimension reduction with small datasets

We consider supervised dimension reduction problems, namely to identify ...

0 Xin Cai, et al. ∙

research

∙ 08/15/2018

Trimmed Ensemble Kalman Filter for Nonlinear and Non-Gaussian Data Assimilation Problems

We study the ensemble Kalman filter (EnKF) algorithm for sequential data...

0 Weixuan Li, et al. ∙

research

∙ 07/12/2018

CT-GAN: Conditional Transformation Generative Adversarial Network for Image Attribute Modification

We propose a novel, fully-convolutional conditional generative model cap...

6 Sangpil Kim, et al. ∙

Guang Lin

Featured Co-authors