Research

Talks

• 2025.07.04 Hong Kong, HKSIAM 2025
• 2025.06.30 Manila, EASIAM 2025
• 2024.07.17 Singapore, SciCADE 2024
• 2023.08.19 Tokyo, ICIAM2023 Tokyo
• 2023.05.29 Shenzhen, The 12th National Conference on Inverse Problems, Imaging and Applications
• 2021.04.07 Changsha, CSUST, Computational inverse problem and its application workshop
• 2019.10.11 Guilin, National Symposium on Experimental Design and Statistical Science
• CSIAM 2019, 2020, 2021, 2023, 2024, 2025

Publications

Statistical computation [SC] - Parameter inference and inversion [INV] - Failure probability estimation [FP] - Tansition state and minimum energy path calcualtion [TS] - Optical Engineering [OE] - Computational diffraction imaging [CDI]

• 刘艺菲，黄子言，吴江琦，范斌，王洪桥 (2025). 衍射成像中的点扩散函数修正技术综述. 光电工程. 52(12): 250294. [CDI]

• Jiao Li, Haocheng Mei, Qinfeng Ou, Hongqiao Wang, Jinyong Ying (2025). Neural networks for solving elliptic problems: analysis and adaptivities, Applied Mathematical Modelling, 116716. [SC]

• Pucheng Tang, Hongqiao Wang, Qian Chen, Wenzhou Lin, Heng Yong (2025). Gaussian process surrogate with physical law-corrected prior for multi-coupled PDEs defined on irregular geometry, https://arxiv.org/abs/2509.02617. [SC,INV]

• Miao Huang, Hongqiao Wang, Kunyu Wu (2025). Accelerated Bayesian Optimal Experimental Design via Conditional Density Estimation and Informative Data, https://arxiv.org/pdf/2507.15235. [SC]

• Hongji Wang, Hongqiao Wang, Jinyong Ying, Qingping Zhou (2025). Sequential Bayesian Design for Efficient Surrogate Construction in the Inversion of Darcy Flows, https://arxiv.org/pdf/2507.17713. [INV]

• Hongdan Zheng, Hongqiao Wang, Pei Yin, Lina Li, Xiaofei Guan (2026). Adaptive parallel design criterion for failure probability estimation with Student-t likelihood, Reliability Engineering & System Safety, 111493. [FP]

• Jinglai Li, Hongqiao Wang (2025). Gaussian Processes Regression for Uncertainty Quantification: An Introductory Tutorial, arXiv:2502.03090.[SC]

• Ying Zhou, Jinglai Li, Xiang Zhou, Hongqiao Wang (2024). Model-Embedded Gaussian Process Regression for Parameter Estimation in Dynamical System, arxiv.[SC,INV]

• Jinyong Ying, Yaqi Xie, Jiao Li, Hongqiao Wang (2024). Accurate adaptive deep learning method for solving elliptic problems. Communications in Computational Physics, 37(3), 849-876. [SC]

• Zheng Hu, Hongqiao Wang, Qingping Zhou (2024). A MCMC method based on surrogate model and Gaussian process parameterization for infinite Bayesian PDE inversion. Journal of Computational Physics, 507, 112970. [INV]

• Tiexin Guo,Hongji Wang, Jinglai Li, Hongqiao Wang (2024). Sampling-based adaptive design strategy for failure probability estimation. Reliability Engineering & System Safety, 241, 109664. [FP]

• Qingping Zhou, Guixian Xu, Zhexin Wen, Hongqiao Wang (2023). Anderson Accelerated Gauss-Newton-guided deep learning for nonlinear inverse problems with Application to Electrical Impedance Tomography. arXiv preprint arXiv:2312.12693. [INV]

• Xin Cai, Jingyu Yang, Zhibao Li, Hongqiao Wang, Miao Huang (2023). Simulation-based transition density approximation for the inference of SDE models. arXiv preprint arXiv:2401.02529. [SC,INV]

• Shuting Gu, Hongqiao Wang, Xiang Zhou (2022). Active Learning for Saddle Point Calculation. Journal of Scientific Computing, 93(3), 78. [TS]

• Xin Cai, Junda Xiong,Hongqiao Wang, Jinglai Li (2022). Control variates with a dimension reduced Bayesian Monte Carlo sampler. International Journal for Uncertainty Quantification, 12(4). [SC]

• Ying Zhou, Qingping Zhou, Hongqiao Wang (2022). Inferring the unknown parameters in differential equation by Gaussian process regression with constraint. Computational and Applied Mathematics, 41(6), 280. [INV]

• Hongqiao Wang, Ziqiao Ao, Tengchao Yu, Jinglai Li (2021). Inverse Gaussian Process regression for likelihood-free inference. arXiv preprint arXiv:2102.10583. [SC, INV]

• Tengchao Yu, Hongqiao Wang, Jinglai Li (2021). Maximum conditional entropy hamiltonian monte carlo sampler. SIAM Journal on Scientific Computing, 43(5), A3607-A3626. [SC]

• Hongqiao Wang, Xiang Zhou (2021). Explicit estimation of derivatives from data and differential equations by Gaussian process regression. International Journal for Uncertainty Quantification, 11(4). [SC]

• Hongqiao Wang, Jinglai Li (2018). Adaptive Gaussian process approximation for Bayesian inference with expensive likelihood functions. Neural computation, 30(11), 3072-3094. [SC,INV]

• Hongqiao Wang, Guang Lin, Jinglai Li (2016). Gaussian process surrogates for failure detection: A Bayesian experimental design approach. Journal of Computational Physics, 313, 247-259. [FP]

• Hongqiao Wang, Bin Fan, Yongqian Wu, Haitao Liu, Rong Liu (2014). The thin mirror deformation and stress distribution analysis based on different influence functions. SPIE. [OE]

• 王洪桥, 范 斌, 吴永前, 刘海涛, 刘 容 (2014). $\Phi$1.2 m 主镜光学加工中轴向支撑系统的补偿力分析计算. 红外与激光工程, 第 43 卷第 6 期. [OE]

• Hongqiao Wang, Bin Fan, Yongqian Wu, Haitao Liu, Rong Liu, and Fengtao Yan (2013). Influence study of solving correction forces caused by fitting errors for thin meniscus mirror. J. Opt. Soc. Am. A 30, 2409-2414. [OE]