| Adaptive Gaussian process approximation for Bayesian inference with expensive likelihood functions H Wang, J Li Neural computation 30 (11), 3072-3094, 2018 | 62 | 2018 |
| 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 | 33 | 2016 |
| Explicit estimation of derivatives from data and differential equations by Gaussian process regression H Wang, X Zhou International Journal for Uncertainty Quantification 11 (4), 2021 | 12 | 2021 |
| Maximum conditional entropy hamiltonian monte carlo sampler T Yu, H Wang, J Li SIAM Journal on Scientific Computing 43 (5), A3607-A3626, 2021 | 4 | 2021 |
| Influence study of solving correction forces caused by fitting errors for thin meniscus mirror H Wang, B Fan, Y Wu, H Liu, R Liu, F Yan JOSA A 30 (11), 2409-2414, 2013 | 4 | 2013 |
| Active learning for transition state calculation S Gu, H Wang, X Zhou J. Sci. Comput 93, 78, 2022 | 2 | 2022 |
| Inverse Gaussian Process regression for likelihood-free inference H Wang, Z Ao, T Yu, J Li arXiv preprint arXiv:2102.10583, 2021 | 2 | 2021 |
| Sampling-based adaptive design strategy for failure probability estimation T Guo, H Wang, J Li, H Wang Reliability Engineering & System Safety 241, 109664, 2024 | 1 | 2024 |
| Active Learning for Saddle Point Calculation S Gu, H Wang, X Zhou Journal of Scientific Computing 93 (3), 78, 2022 | 1 | 2022 |
| Simulation-based transition density approximation for the inference of SDE models X Cai, J Yang, Z Li, H Wang arXiv preprint arXiv:2401.02529, 2023 | | 2023 |
| Anderson Accelerated Gauss-Newton-guided deep learning for nonlinear inverse problems with Application to Electrical Impedance Tomography Q Zhou, G Xu, Z Wen, H Wang arXiv preprint arXiv:2312.12693, 2023 | | 2023 |
| Adaptive design of experiment via normalizing flows for failure probability estimation H Wang, T Guo, J Li, H Wang arXiv preprint arXiv:2302.06837, 2023 | | 2023 |
| Inferring the unknown parameters in differential equation by Gaussian process regression with constraint Y Zhou, Q Zhou, H Wang Computational and Applied Mathematics 41 (6), 280, 2022 | | 2022 |
| Control variates with a dimension reduced Bayesian Monte Carlo sampler X Cai, J Xiong, H Wang, J Li International Journal for Uncertainty Quantification 12 (4), 2022 | | 2022 |
| Maximizing conditional entropy of Hamiltonian Monte Carlo sampler T Yu, H Wang, J Li arXiv preprint arXiv:1910.05275, 2019 | | 2019 |
