| 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 | 135 | 2011 |
| 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 | 22 | 2018 |
| 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 |
| Gaussian Process Regression and Conditional Polynomial Chaos for Parameter Estimation J Li, AM Tartakovsky Journal of Computational Physics 416, 2019 | 11 | 2019 |
| A unified framework for mesh refinement in random and physical space J Li, P Stinis Journal of Computational Physics 323, 243-264, 2016 | 11 | 2016 |
| 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 penalty 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 | 6 | 2022 |
| 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 | 4 | 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 UniversityVerified email at osu.edu
