Evaluation of failure probability via surrogate models J Li, D Xiu
Journal of Computational Physics 229 (23), 8966-8980, 2010
196 2010 An efficient surrogate-based method for computing rare failure probability J Li, J Li, D Xiu
Journal of Computational Physics 230 (24), 8683-8697, 2011
134 2011 Computation of failure probability subject to epistemic uncertainty J Li, D Xiu
SIAM Journal on Scientific Computing 34 (6), A2946-A2964, 2012
20 2012 Mori-Zwanzig reduced models for uncertainty quantification J Li, P Stinis
Journal of Computational Dynamics 6 (1), 39-68, 2019
12 2019 A unified framework for mesh refinement in random and physical space J Li, P Stinis
Journal of Computational Physics 323, 243-264, 2016
12 2016 Gaussian Process Regression and Conditional Polynomial Chaos for Parameter Estimation J Li, AM Tartakovsky
Journal of Computational Physics 416, 2019
11 2019 Surrogate Based Method for Evaluation of Failure Probability under Multiple Constraints J Li, D Xiu
SIAM Journal on Scientific Computing 36 (2), A828-A845, 2014
10 2014 On upper and lower bounds for quantity of interest in problems subject to epistemic uncertainty J Li, X Qi, D Xiu
SIAM Journal on Scientific Computing 36 (2), A364-A376, 2014
9 2014 Physics-informed Karhunen-Loéve and neural network approximations for solving inverse differential equation problems J Li, AM Tartakovsky
Journal of Computational Physics 462, 111230, 2022
8 2022 A data-driven framework for sparsity-enhanced surrogates with arbitrary mutually dependent randomness H Lei, J Li, P Gao, P Stinis, NA Baker
Computer Methods in Applied Mechanics and Engineering 350, 199-227, 2019
7 2019 Stochastic collocation methods via minimization of Transformed L Guo, J Li, Y Liu
East Asian Journal on Applied Mathematics 8 (3), 566-585, 2018
7 2018 Mesh refinement for uncertainty quantification through model reduction J Li, P Stinis
Journal of Computational Physics 280, 164-183, 2015
7 2015 Model reduction for a power grid model J Li, P Stinis
Journal of Computational Dynamics 9 (1), 1-26, 2022
5 2022 When Bifidelity Meets CoKriging: An Efficient Physics-Informed Multifidelity Method X Yang, X Zhu, J Li
SIAM J. Sci. Comput. 42 (1), A220–A249. (30 pages), 2018
5 2018 Annealed adaptive importance sampling method in PINNs for solving high dimensional partial differential equations Z Zhang, J Li, B Liu
Journal of Computational Physics 521, 113561, 2025
3 2025 Gaussian process regression and conditional Karhunen-Loéve expansion for forward uncertainty quantification and inverse modeling in the presence of measurement noise J Li, AM Tartakovsky
Journal of Machine Learning for Modeling and Computing 3 (2), 2022
3 2022 Improving solution accuracy and convergence for stochastic physics parameterizations with colored noise P Stinis, H Lei, J Li, H Wan
Monthly Weather Review 148 (6), 2251-2263, 2019
3 2019 Data Generation-based Operator Learning for Solving Partial Differential Equations on Unbounded Domains J Wang, X Wang, J Li, B Liu
arXiv preprint arXiv:2309.02446, 2023
2 2023 Theoretical Development of Controller Transfer applied to Dynamical Systems I Chakraborty, Y Chen, J Li, D Vrabie
Proceedings of the Eleventh ACM International Conference on Future Energy …, 2020
2 2020 A Review of the Bayesian Method in Nuclear Fusion Diagnostic Research C Wang, J Li, Y Wei, Z Wang, R Yang, D Li, Z Yang, Z Zhao
Journal of Fusion Energy 43 (1), 11, 2024
2024 
Dongbin XiuProfessor of Mathematics, The Ohio State UniversityCorreu electrònic verificat a osu.edu
