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• Author = Cui T     [web profile page]

1. Cui T, De Sterck H, Gilbert AD, Polishchuk S, Scheichl R
Tiangang Cui, Hans De Sterck, Alexander D Gilbert, Stanislav Polishchuk, Robert Scheichl: Multilevel Monte Carlo Methods for Stochastic Convection–Diffusion Eigenvalue Problems, Journal of Scientific Computing, 99 (2024), Article 77 (34 pages).

2. Cui T, Detommaso G, Scheich R
Tiangang Cui, Gianluca Detommaso and Robert Scheich: Multilevel dimension-independent likelihood-informed MCMC for large-scale inverse problems, Inverse Problems, 40 (2024), no. 3, 035005 (33 pages).

3. Cui T, Dolgov S, Scheichl R
Tiangang Cui, Sergey Dolgov and Robert Scheichl: Deep Importance Sampling Using Tensor Trains with Application to a Priori and a Posteriori Rare Events, SIAM Journal on Scientific Computing, 46 (2024), no. 1, C1–C29.

4. Cui T, Dolgov S, Zahm O
Tiangang Cui, Sergey Dolgov, Olivier Zahm: Scalable conditional deep inverse Rosenblatt transports using tensor trains and gradient-based dimension reduction, Journal of Computational Physics, 485 (2023), 112103.

5. Cui T, Wang Q, Zhang Z
Tiangang Cui, Zhongjian Wang, Zhiwen Zhang: A variational neural network approach for glacier modelling with nonlinear rheology, Communications in Computational Physics, 34 (2023), no. 4, 934–954.

6. Cui T, Tong XT, Zahm O
Tiangang Cui, Xin T. Tong and Olivier Zahm: Prior normalization for certified likelihood-informed subspace detection of Bayesian inverse problems, Inverse Problems, 38 (2022), 124002.

7. Cui T, Dolgov S
Tiangang Cui & Sergey Dolgov: Deep composition of tensor trains using squared inverse Rosenblatt transports, Foundations of Computational Mathematics, 22 (2022), 1863–1922.

8. Cui T, Tong XT
Tiangang Cui, Xin T. Tong: A unified performance analysis of likelihood-informed subspace methods, Bernoulli, 28 (2022), 2788–2815.

9. Zahm O, Cui T, Law K, Spantini A, Marzouk Y
Olivier Zahm, Tiangang Cui, Kody Law, Alessio Spantini and Youssef Marzouk: Certified dimension reduction in nonlinear Bayesian inverse problems, Mathematics of Computation, 91 (2022), 1789–1835.

10. Cui T, Zahm O
Tiangang Cui and Olivier Zahm: Data-free likelihood-informed dimension reduction of Bayesian inverse problems, Inverse Problems, 37 (2021), 045009.

11. Bian L, Cui T, Yeo BTT, Fornito A, Razi A, Keith J
Lingbin Bian, Tiangang Cui, B.T. Thomas Yeo, Alex Fornito, Adeel Razi, Jonathan Keith: Identification of community structure-based brain states and transitions using functional MRI, NeuroImage, 244 (2021), 118635.

12. Bardsley JM, Cui T
Johnathan M. Bardsley and Tiangang Cui: Optimization-based Markov chain Monte Carlo methods for nonlinear hierarchical statistical inverse problems, SIAM/ASA Journal on Uncertainty Quantification,, 9 (2021), 29–64.

13. Bardsley JM, Cui T, Youssef M, Wang Z
Johnathan M. Bardsley, Tiangang Cui, Youssef M. Marzouk and Zheng Wang: Scalable optimization-based sampling on function space, SIAM Journal on Scientific Computing, 42 (2020), A1317–A1347.

14. Wu S, Cui T, Zhang X, Tian T
Siyuan Wu, Tiangang Cui, Xinan Zhang, Tianhai Tian: A non-linear reverse-engineering method for inferring genetic regulatory networks, PeerJ, na (2020), e9065.

15. Brown RD, Bardsley JM, Cui T
Richard D Brown, Johnathan M Bardsley and Tiangang Cui: Semivariogram methods for modeling Whittle–Matérn priors in Bayesian inverse problems, Inverse Problems, 36 (2020), 055006.

16. Fox C, Cui T, Neumayer M
Colin Fox, Tiangang Cui & Markus Neumayer: GEM Randomized reduced forward models for efficient Metropolis–Hastings MCMC, with application to subsurface fluid flow and capacitance tomography, GEM-International Journal on Geomathematics, 11 (2020), 1–38.

17. Bardsley JM, Cui T
Johnathan M. Bardsley and Tiangang Cui: A Metropolis-Hastings-within-Gibbs sampler for nonlinear hierarchical-Bayesian inverse problems, 2017 MATRIX Annals, MATRIX Book Series, Springer, Netherlands, (2019), 3–12. ISBN 978-3-030-04160-1.

18. Cui T, Fox C, O'Sullivan MJ
Tiangang Cui, Colin Fox and Michael J. O'Sullivan: A posteriori stochastic correction of reduced models in delayed-acceptance MCMC, with application to multiphase subsurface inverse problems, International Journal for Numerical Methods in Engineering, 118 (2019), no. 10, 578–605.

19. Cui T, Fox C, O'Sullivan MJ, Nicholls GK
T. Cui, C. Fox, M. J. O'Sullivan and G. K. Nicholls: Using parallel Markov chain Monte Carlo to quantify uncertainties in geothermal reservoir calibration, International Journal for Uncertainty Quantification, 9 (2019), no. 3, 295–310.

20. Ye N, Roosta-Khorasani F, Cui T
Nan Ye, Farbod Roosta-Khorasani and Tiangang Cui: Optimization methods for inverse problems, 2017 MATRIX Annals, MATRIX Book Series, Springer, Netherlands, (2019), 121–140. ISBN 978-3-030-04160-1.

21. Thiele ST, Grose L, Cui T, Cruden AR, Micklethwaite S
Samuel T. Thiele, Lachlan Grose, Tiangang Cui, Alexander R. Cruden, Steven Micklethwaite: Extraction of high-resolution structural orientations from digital data: A Bayesian approach, Journal of Structural Geology, 122 (2019), 106–115.

22. Reboul CF, Kiesewetter S, Eager M, Belousoff M, Cui T, De Sterck H, Elmlund D, Elmlund H
Cyril F. Reboul, Simon Kiesewetter, Michael Eager, Matthew Belousoff, Tiangang Cui, Hans De Sterck, Dominika Elmlund and Hans Elmlund: Rapid near-atomic resolution single-particle 3D reconstruction with SIMPLE, Journal of structural biology, 204 (2018), no. 2, 172–181.

23. Wu S, Cui T, Tian T
Siyuan Wu, Tiangang Cui, Tianhai Tian: Mathematical modelling of genetic network for regulating the fate determination of hematopoietic stem cells, 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2018 IEEE International Conference on Bioinformatics and Biomedicine, Huiru (Jane) Zheng, Zoraida Callejas, David Griol, Haiying Wang, Xiaohua Hu, Harald Schmidt, Jan Baumbach, Julie Dickerson, Le Zhang (eds.), IEEE, U.S.A., (2018), 2167–2173. ISBN 978-1-5386-5488-0.

24. Detommaso G, Cui T, Spantini A, Marzouk Y, Scheichl R
Gianluca Detommaso, Tiangang Cui, Alessio Spantini, Youssef Marzouk and Robert Scheichl: A Stein variational Newton method, Advances in neural information processing systems, Advances in Neural Information Processing Systems, Samy Bengio, Hanna M. Wallach, Hugo Larochelle, Kristen Grauman, Nicolò Cesa-Bianchi (eds.), Curran Associates Inc., 57 Morehouse Lane, Red Hook, NY, United States, (2018), 9187––9197. ISBN 9781510884472.

25. Spantini A, Cui T, Willcox K, Tenorio L, Marzouk Y
Alessio Spantini, Tiangang Cui, Karen Willcox, Luis Tenorio and Youssef Marzouk: Goal-oriented optimal approximations of Bayesian linear inverse problems, SIAM Journal on Scientific Computing, 39 (2017), no. 5, S167–S196.

26. Wang Z, Bardsley J, Solonen A, Cui T, Marzouk Y
Zheng Wang, John Bardsley, Antti Solonen, Tiangang Cui, Youssef Marzouk: Bayesian Inverse Problems with l-1 Priors: A Randomize-Then-Optimize Approach, SIAM Journal on Scientific Computing, 39 (2017), no. 5, S140–S166.

27. Cui T, Fox C, O'Sullivan MJ
T. Cui, C. Fox and M.J. O'Sullivan: Bayesian calibration of a large-scale geothermal reservoir model by a new adaptive delayed acceptance Metropolis Hastings algorithm, Water Resources Research, 47 (2011), no. 10, W10521.