| Adaptive construction of surrogates for the Bayesian solution of inverse problems J Li, YM Marzouk SIAM Journal on Scientific Computing 36 (3), A1163-A1186, 2014 | 155 | 2014 |
| 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 | 140 | 2011 |
| Gradual Crossover from Subdiffusion to Normal Diffusion: A Many-Body Effect in Protein Surface Water P Tan, Y Liang, Q Xu, E Mamontov, J Li, X Xing, L Hong Physical Review Letters 120 (24), 248101, 2018 | 93 | 2018 |
| Adaptive Gaussian process approximation for Bayesian inference with expensive likelihood functions H Wang, J Li Neural computation 30 (11), 3072-3094, 2018 | 77 | 2018 |
| Bayesian inference and uncertainty quantification for medical image reconstruction with Poisson data Q Zhou, T Yu, X Zhang, J Li SIAM Journal on Imaging Sciences 13 (1), 29-52, 2020 | 40 | 2020 |
| Gaussian process surrogates for failure detection: a Bayesian experimental design approach H Wang, G Lin, J Li Journal of Computational Physics 313, 247-259, 2016 | 36 | 2016 |
| A TV-Gaussian prior for infinite-dimensional Bayesian inverse problems and its numerical implementations Z Yao, Z Hu, J Li Inverse Problems 32, 075006:1-19, 2016 | 35 | 2016 |
| An adaptive reduced basis ANOVA method for high-dimensional Bayesian inverse problems Q Liao, J Li Journal of Computational Physics 396, 364-380, 2019 | 33 | 2019 |
| On an adaptive preconditioned Crank-Nicolson MCMC algorithm for infinite dimensional Bayesian inferences Z Hu, Z Yao, J Li Journal of Computational Physics 332, 492-503, 2017 | 28 | 2017 |
| Increasing the efficiency of Sequential Monte Carlo samplers through the use of approximately optimal L-kernels PL Green, LJ Devlin, RE Moore, RJ Jackson, J Li, S Maskell Mechanical Systems and Signal Processing 162, 108028, 2022 | 27 | 2022 |
| Noise-induced perturbations of dispersion-managed solitons J Li, E Spiller, G Biondini Physical Review A—Atomic, Molecular, and Optical Physics 75 (5), 053818, 2007 | 24 | 2007 |
| NF-ULA: Normalizing flow-based unadjusted Langevin algorithm for imaging inverse problems Z Cai, J Tang, S Mukherjee, J Li, CB Schönlieb, X Zhang SIAM Journal on Imaging Sciences 17 (2), 820-860, 2024 | 23* | 2024 |
| A note on the Karhunen–Loève expansions for infinite-dimensional Bayesian inverse problems J Li Statistics & Probability Letters 106, 1-4, 2015 | 20 | 2015 |
| Anisotropic hinge model for polarization-mode dispersion in installed fibers J Li, G Biondini, WL Kath, H Kogelnik Optics letters 33 (16), 1924-1926, 2008 | 18 | 2008 |
| Nonlocal TV-Gaussian prior for Bayesian inverse problems with applications to limited CT reconstruction. D Lv, Q Zhou, JK Choi, J Li, X Zhang Inverse Problems & Imaging 14 (1), 2020 | 17 | 2020 |
| An approximate empirical Bayesian method for large-scale linear-Gaussian inverse problems Q Zhou, W Liu, J Li, YM Marzouk Inverse Problems 34 (9), 2018 | 17 | 2018 |
| An Adaptive Independence Sampler MCMC Algorithm for Bayesian Inferences of Functions Z Feng, J Li SIAM Journal on Scientific Computing 40 (3), A1301–A1321, 2018 | 17* | 2018 |
| Clustered active-subspace based local Gaussian Process emulator for high-dimensional and complex computer models J Xiong, X Cai, J Li Journal of Computational Physics 450, 110840, 2022 | 16 | 2022 |
| A subset multicanonical Monte Carlo method for simulating rare failure events X Chen, J Li Journal of Computational Physics 344, 23-35, 2017 | 16 | 2017 |
| A surrogate accelerated multicanonical Monte Carlo method for uncertainty quantification K Wu, J Li Journal of Computational Physics 321, 1098-1109, 2016 | 16 | 2016 |
Wang HongqiaoCentral South UniversityVerified email at csu.edu.cn
