Guannan Zhang

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Soavtorji

Clayton WebsterUniversity of Texas at AustinPreverjeni e-poštni naslov na utexas.edu
Dan LuOak Ridge National LaboratoryPreverjeni e-poštni naslov na ornl.gov
Feng BaoAssociate Professor of Mathematics, Florida State UniversityPreverjeni e-poštni naslov na math.fsu.edu
Hoang TranOak Ridge National LaboratoryPreverjeni e-poštni naslov na ornl.gov
Jiaxin ZhangIntuit AI Research; Oak Ridge National Lab; Johns Hopkins UniversityPreverjeni e-poštni naslov na intuit.com
Diego del-Castillo-NegreteDistinguished R&D Scientist, Oak Ridge National LaboratoryPreverjeni e-poštni naslov na ornl.gov
Minglei YangOak Ridge National LaboratoryPreverjeni e-poštni naslov na ornl.gov
Sirui BiWalmart Global Tech;Oak Ridge National Laboratory; Johns Hopkins UniversityPreverjeni e-poštni naslov na jhu.edu
Weidong ZhaoShandong UniversityPreverjeni e-poštni naslov na sdu.edu.cn
Junqi YinNational Center for Computational Sciences, Oak Ridge National LaboratoryPreverjeni e-poštni naslov na ornl.gov
Zezhong ZhangPost-doc, Oak Ridge National LaboratoryPreverjeni e-poštni naslov na ornl.gov
Lili JuProfessor of Mathematics, University of South CarolinaPreverjeni e-poštni naslov na math.sc.edu
yanzhao caoProfessor of Mathematics, Auburn UniversityPreverjeni e-poštni naslov na auburn.edu
Siyan LiuOak Ridge National LaboratoryPreverjeni e-poštni naslov na ornl.gov
Ming YeFlorida State UniversityPreverjeni e-poštni naslov na fsu.edu
Viktor ReshniakOak Ridge National LaboratoryPreverjeni e-poštni naslov na ornl.gov
Yanfang LiuAssistant Professor, Middle Tennessee State UniversityPreverjeni e-poštni naslov na mtsu.edu
Pei ZhangOak Ridge National LaboratoryPreverjeni e-poštni naslov na ornl.gov
Huibo CaoOak Ridge National LaboratoryPreverjeni e-poštni naslov na ornl.gov
Peter JantschWheaton CollegePreverjeni e-poštni naslov na wheaton.edu

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Guannan Zhang

Oak Ridge National Laboratory

Preverjeni e-poštni naslov na ornl.gov - Domača stran

High-dimensional Approximation Numerical Analysis Machine Learning Uncertainty Quantification


Naslov Razvrsti po navedbah Razvrsti po letniku Razvrsti po naslovu	Navedeno Navedeno	Leto
Stochastic finite element methods for partial differential equations with random input data MD Gunzburger, CG Webster, G Zhang Acta Numerica 23, 521-650, 2014	275	2014
A stable multistep scheme for solving backward stochastic differential equations W Zhao, G Zhang, L Ju SIAM Journal on Numerical Analysis 48 (4), 1369-1394, 2010	111	2010
An adaptive sparse‐grid high‐order stochastic collocation method for Bayesian inference in groundwater reactive transport modeling G Zhang, D Lu, M Ye, M Gunzburger, C Webster Water Resources Research 49 (10), 6871-6892, 2013	98	2013
Robust data-driven approach for predicting the configurational energy of high entropy alloys J Zhang, X Liu, S Bi, J Yin, G Zhang, M Eisenbach Materials & Design 185, 108247, 2020	77	2020
A Taylor expansion‐based adaptive design strategy for global surrogate modeling with applications in groundwater modeling S Mo, D Lu, X Shi, G Zhang, M Ye, J Wu, J Wu Water Resources Research 53 (12), 10802-10823, 2017	68	2017
A sparse-grid method for multi-dimensional backward stochastic differential equations G Zhang, M Gunzburger, W Zhao Journal of Computational Mathematics, 221-248, 2013	66	2013
Error analysis of a stochastic collocation method for parabolic partial differential equations with random input data G Zhang, M Gunzburger SIAM Journal on Numerical Analysis 50 (4), 1922-1940, 2012	66	2012
A Generalized θ-Scheme for Solving Backward Stochastic Differential Equations. W Zhao, Y Li, G Zhang Discrete & Continuous Dynamical Systems-Series B 17 (5), 2012	61	2012
Analysis of quasi-optimal polynomial approximations for parameterized PDEs with deterministic and stochastic coefficients H Tran, C Webster, G Zhang numerische mathematik 137 (2), 451-493, 2017	60	2017
An adaptive wavelet stochastic collocation method for irregular solutions of partial differential equations with random input data M Gunzburger, CG Webster, G Zhang Sparse Grids and Applications-Munich 2012, 137-170, 2014	56*	2014
Learning nonlinear level sets for dimensionality reduction in function approximation G Zhang, J Zhang, J Hinkle Advances in Neural Information Processing Systems 32, 2019	49*	2019
On the Lebesgue constant of weighted Leja points for Lagrange interpolation on unbounded domains P Jantsch, CG Webster, G Zhang IMA Journal of Numerical Analysis 39 (2), 1039-1057, 2019	40	2019
An improved multilevel Monte Carlo method for estimating probability distribution functions in stochastic oil reservoir simulations D Lu, G Zhang, C Webster, C Barbier Water Resources Research 52 (12), 9642–9660, 2016	40	2016
A hybrid sparse-grid approach for nonlinear filtering problems based on adaptive-domain of the Zakai equation approximations F Bao, Y Cao, C Webster, G Zhang SIAM/ASA Journal on Uncertainty Quantification 2 (1), 784-804, 2014	40	2014
Non-intrusive inference reduced order model for fluids using deep multistep neural network X Xie, G Zhang, CG Webster Mathematics 7 (8), 757, 2019	38	2019
Split-step Milstein methods for multi-channel stiff stochastic differential systems V Reshniak, AQM Khaliq, DA Voss, G Zhang Applied Numerical Mathematics 89, 1-23, 2015	36	2015
Hyperspherical sparse approximation techniques for high-dimensional discontinuity detection G Zhang, CG Webster, M Gunzburger, J Burkardt SIAM review 58 (3), 517-551, 2016	33*	2016
A directional Gaussian smoothing optimization method for computational inverse design in nanophotonics J Zhang, S Bi, G Zhang Materials & Design 197, 109213, 2021	28	2021
A backward Monte-Carlo method for time-dependent runaway electron simulations G Zhang, D del-Castillo-Negrete Physics of Plasmas 24 (9), 2017	28	2017
Calibration of an agricultural-hydrological model (RZWQM2) using surrogate global optimization M Xi, D Lu, D Gui, Z Qi, G Zhang Journal of Hydrology 544, 456-466, 2017	28	2017

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