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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
Gao, S, Zhang, J, Hu, Y, Wu, Y, Li, L, Hu, Q, Lou, X, Zhu, X, Jiang, J & Ren, W 2023, 'Multifunctional Optical Tomography System With High-Fidelity Surface Extraction Based on a Single Programmable Scanner and Unified Pinhole Modeling', IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/TBME.2023.3336334, https://doi.org/10.1109/TBME.2023.3336334
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
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