| Stochastic finite element methods for partial differential equations with random input data MD Gunzburger, CG Webster, G Zhang Acta Numerica 23, 521-650, 2014 | 272 | 2014 |
| 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 | 100 | 2013 |
| 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 | 100 | 2010 |
| 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 | 70 | 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 | 65 | 2017 |
| 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 | 63 | 2012 |
| A sparse-grid method for multi-dimensional backward stochastic differential equations G Zhang, M Gunzburger, W Zhao Journal of Computational Mathematics, 221-248, 2013 | 62 | 2013 |
| 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 | 58 | 2017 |
| 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 | 58 | 2012 |
| 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 | 53* | 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 | 48* | 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 | 39 | 2016 |
| 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 | 37 | 2019 |
| Non-intrusive inference reduced order model for fluids using deep multistep neural network X Xie, G Zhang, CG Webster Mathematics 7 (8), 757, 2019 | 35 | 2019 |
| 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 | 35 | 2014 |
| 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 | 33 | 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 | 32* | 2016 |
| 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 | 29 | 2017 |
| A directional Gaussian smoothing optimization method for computational inverse design in nanophotonics J Zhang, S Bi, G Zhang Materials & Design 197, 109213, 2021 | 27 | 2021 |
| A backward Monte-Carlo method for time-dependent runaway electron simulations G Zhang, D del-Castillo-Negrete Physics of Plasmas 24 (9), 2017 | 26 | 2017 |
Clayton WebsterUniversity of Texas at AustinVerified email at utexas.edu