Research

Talks

• 2024.07.17 Singapore, SciCADE 2024
• 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 [SC] - Parameter inference and inversion [INV] - Failure probability estimation [FP] - Tansition state and minimum energy path calcualtion [TS]

• Zhou Y., Li J., Zhou X. and Wang H. (2024). Model-Embedded Gaussian Process Regression for Parameter Estimation in Dynamical System, arxiv.[SC, INV]

• Ying, J., Xie, Y., Li, J., & Wang, H. (2024). Accurate adaptive deep learning method for solving elliptic problems. accepted by Communications in Computational Physics. [SC]

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

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

• Zhou, Q., Xu, G., Wen, Z., & Wang, H. (2023). Anderson Accelerated Gauss-Newton-guided deep learning for nonlinear inverse problems with Application to Electrical Impedance Tomography. arXiv preprint arXiv:2312.12693. [INV]

• Cai, X., Yang, J., Li, Z., & Wang, H. (2023). Simulation-based transition density approximation for the inference of SDE models. arXiv preprint arXiv:2401.02529. [SC, INV]

• Gu, S., Wang, H., & Zhou, X. (2022). Active Learning for Saddle Point Calculation. Journal of Scientific Computing, 93(3), 78. [TS]

• Cai, X., Xiong, J., Wang, H., & Li, J. (2022). Control variates with a dimension reduced Bayesian Monte Carlo sampler. International Journal for Uncertainty Quantification, 12(4). [SC]

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

• Wang, H., Ao, Z., Yu, T., & Li, J. (2021). Inverse Gaussian Process regression for likelihood-free inference. arXiv preprint arXiv:2102.10583. [SC, INV]

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

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

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

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