Research
Talks
- 2023.10.13 Kunming, CSIAM 2023
- 2023.08.19 Tokyo, ICIAM2023 Tokyo
- 2023.05.29 Shenzhen, The 12th National Conference on Inverse Problems, Imaging and Applications
- 2021.10.07 Hefei, CSIAM 2021
- 2021.04.07 Changsha, CSUST, Computational inverse problem and its application workshop
- 2020.10.31 Changsha, CSIAM 2020
- 2019.10.11 Guilin, National Symposium on Experimental Design and Statistical Science
- 2019.09.19 Foshan, CSIAM 2019
Publications
Statistical computation
Wang Hongqiao, Ao Ziqiao, Yu Tengchao and Li Jinglai*. Inverse Gaussian Process regression for likelihood-free inference, arXiv preprint.
Wang Hongqiao and Li Jinglai*. Adaptive Gaussian process approximation for Bayesian inference with expensive likelihood functions[J]. Neural Computation, 2018, 30(11): 3072-3094.
Yu Tengchao, Wang Hongqiao and Li Jinglai*. Maximum conditional entropy Hamiltonian Monte Carlo sampler[J]. SIAM Journal on Scientific Computing, 2021, 43(5): A3607-A3626.
Cai Xin, Xiong Junda, Wang Hongqiao* and Li Jinglai. Control variates with a dimension reduced Bayesian Monte Carlo sampler. International Journal for Uncertainty Quantificationr[J]. International Journal for Uncertainty Quantification, 2022, 12(4).
Parameter inference and inverse problem
Cai Xin, Yang Jingyu, Li Zhibao and Wang Hongqiao*. Simulation-based transition density approximation for the inference of SDE models, arxiv.
Zhou Qingping, Xu Guixian, Wen Zhexin and Wang Hongqiao*. Anderson Accelerated Gauss-Newton-guided deep learning for nonlinear inverse problems with Application to Electrical Impedance Tomography. arxiv.
Hu Zheng, Wang Hongqiao* and Zhou Qingping. A MCMC method based on surrogate model and Gaussian process parameterization for infinite Bayesian PDE inversion. accepted for publication in Journal of Computational Physics.
Wang Hongqiao* and Zhou Xiang. Explicit estimation of derivatives from data and differential equations by gaussian process regression[J]. International Journal for Uncertainty Quantification, 2021, 11(4).
Zhou Ying, Zhou Qingping. and Wang Hongqiao*. Inferring the unknown parameters in differential equation by Gaussian process regression with constraint. Comp. Appl. Math. 41, 280 (2022).
Failure probability estimation
Wang Hongqiao, Lin Guang and Li Jinglai*. Gaussian process surrogates for failure detection: A Bayesian experimental design approach[J]. Journal of Computational Physics, 2016, 313: 247-259.
Guo Tiexin, Wang Hongji, Li Jinglai and Wang Hongqiao*. Sampling-based adaptive design strategy for failure probability estimation[J]. Reliability Engineering & System Safety, 2024, 241: 109664.
Tansition state and minimum energy path calcualtion
- Gu Shuting, Wang Hongqiao* and Zhou Xiang. Active Learning for Saddle Point Calculation[J]. Journal of Scientific Computing, 2022, 93(3): 1-24.