个人简介

Dr Jiahua Jiang, Assistant Professor.

联系方式

邮件：j.jiang.3@bham.ac.uk

任职及教育经历

PhD in Engineering and Applied Science, University of Massachusetts Dartmouth, US, 2018

BS in Applied Mathematics, University of Science and Technology of China, 2013

研究领域

Inverse Problem and Imaging

Model Order Reduction

Uncertainty Quantification

近期学术出版物

Chung, J, Jiang, J, Miller, S & Saibaba, A 2023, 'Hybrid Projection Methods for Solution Decomposition in Large-Scale Bayesian Inverse Problems', SIAM Journal on Scientific Computing, pp. S97-S119. https://doi.org/10.1137/22M1502197

Guo, R, Jiang, J & Li, Y 2023, 'Learn an Index Operator by CNN for Solving Diffusive Optical Tomography: A Deep Direct Sampling Method', Journal of Scientific Computing, vol. 95, no. 1, 31. https://doi.org/10.1007/s10915-023-02115-7

Lin, J, Jiang, C, Jiang, J & Kang, J 2022, 'Conjugate gradient persymmetric adaptive matched filter', Digital Signal Processing, vol. 123, 103395. https://doi.org/10.1016/j.dsp.2022.103395

Jiang, J, Sanogo, F & Navasca, C 2022, 'Low-CP-rank tensor completion via practical regularization', Journal of Scientific Computing, vol. 91, no. 1, 18 . https://doi.org/10.1007/s10915-022-01789-9

Qian, E, Tabeart, JM, Beattie, C, Gugercin, S, Jiang, J, Kramer, PR & Narayan, A 2022, 'Model reduction of linear dynamical systems via balancing for Bayesian inference', Journal of Scientific Computing, vol. 91, no. 1, 29 . https://doi.org/10.1007/s10915-022-01798-8

Guo, R & Jiang, J 2021, 'Construct deep neural networks based on direct sampling methods for solving electrical impedance tomography', SIAM Journal on Scientific Computing, vol. 43, no. 3, pp. B678-B711. https://doi.org/10.1137/20M1367350

Cho, T, Chung, J & Jiang, J 2021, 'Hybrid projection methods for large-scale inverse problems with mixed Gaussian priors', Inverse Problems, vol. 37, no. 4, 044002. https://doi.org/10.1088/1361-6420/abd29d

Jiang, J, Chung, J & Sturler, ED 2021, 'Hybrid projection methods with recycling for inverse problems', SIAM Journal on Scientific Computing, vol. 43, no. 5, pp. S146--S172. https://doi.org/10.1137/20M1349515

Jiang, J & Chen, Y 2020, 'Adaptive greedy algorithms based on parameter-domain decomposition and reconstruction for the reduced basis method', International Journal for Numerical Methods in Engineering, vol. 121, no. 23, pp. 5426-5445. https://doi.org/10.1002/nme.6544

Chen, Y, Jiang, J & Narayan, A 2019, 'A robust error estimator and a residual-free error indicator for reduced basis methods', Computers & Mathematics with Applications, vol. 77, no. 7, pp. 1963-1979. https://doi.org/10.1016/j.camwa.2018.11.032

Chen, Y, Dong, B & Jiang, J 2018, 'Optimally convergent hybridizable discontinuous Galerkin method for fifth-order Korteweg-de Vries type equations', ESAIM: M2AN, vol. 52, no. 6, pp. 2283-2306. https://doi.org/10.1051/m2an/2018037

Jiang, J, Chen, Y & Narayan, A 2017, 'Offline-Enhanced Reduced Basis Method Through Adaptive Construction of the Surrogate Training Set', Journal of Scientific Computing, vol. 73, pp. 853-875. https://doi.org/10.1007/s10915-017-0551-3

Jiang, J, Chen, Y & Narayan, A 2016, 'A Goal-Oriented Reduced Basis Methods-Accelerated Generalized Polynomial Chaos Algorithm', SIAM/ASA Journal on Uncertainty Quantification, vol. 4, no. 1, pp. 1398-1420. https://doi.org/10.1137/16M1055736