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Li Jing
Title
Cited by
Cited by
Year
Evaluation of failure probability via surrogate models
J Li, D Xiu
Journal of Computational Physics 229 (23), 8966-8980, 2010
1502010
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
1062011
Computation of failure probability subject to epistemic uncertainty
J Li, D Xiu
SIAM Journal on Scientific Computing 34 (6), A2946-A2964, 2012
172012
Mori-Zwanzig reduced models for uncertainty quantification
J Li, P Stinis
Journal of Computational Dynamics 6 (1), 39-68, 2019
102019
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
102014
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
92014
A unified framework for mesh refinement in random and physical space
J Li, P Stinis
Journal of Computational Physics 323, 243-264, 2016
82016
Gaussian Process Regression and Conditional Polynomial Chaos for Parameter Estimation
J Li, AM Tartakovsky
Journal of Computational Physics 416, 2019
62019
Mesh refinement for uncertainty quantification through model reduction
J Li, P Stinis
Journal of Computational Physics 280, 164-183, 2015
62015
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
52019
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
52018
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
32018
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
12019
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
2022
Model reduction for a power grid model
J Li, P Stinis
Journal of Computational Dynamics 9 (1), 1-26, 2022
2022
GAUSSIAN PROCESS REGRESSION AND CONDITIONAL KARHUNEN-LOÈVE 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
2022
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
2020
Efficient failure probability calculation through mesh refinement
J Li, P Stinis
arXiv preprint arXiv:1509.06668, 2015
2015
Efficient estimation of failure probability
J Li
Purdue University, 2013
2013
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Articles 1–19