2025

• Cui, T., Dick, J., Pillichshammer, F. (2025). Quasi-Monte Carlo methods for mixture distributions and approximated distributions via piecewise linear interpolation. Advances in Computational Mathematics, 5(1), Article 10 - 1-Article 10 - 44. [More Information]

2024

• Cui, T., Dolgov, S., Scheichl, R. (2024). Deep Importance Sampling Using Tensor Trains with Application to a Priori and a Posteriori Rare Events. SIAM Journal on Scientific Computing, 46(1), C1-C29. [More Information]
• Cui, T., Detommaso, G., Scheichl, R. (2024). Multilevel dimension-independent likelihood-informed MCMC for large-scale inverse problems. Inverse Problems, 40(3), Article number 035005-1-Article number 035005-33. [More Information]
• Cui, T., De Sterck, H., Gilbert, A., Polishchuk, S., Scheichl, R. (2024). Multilevel Monte Carlo Methods for Stochastic Convection–Diffusion Eigenvalue Problems. Journal of Scientific Computing, 99(3), Article 77-1-Article 77-34. [More Information]

2023

• Cui, T., Wang, Z., Zhang, Z. (2023). A variational neural network approach for glacier modelling with nonlinear rheology. Communications in Computational Physics, 34(4), 934-854. [More Information]

2022

• Cui, T., Dolgov, S. (2022). Deep composition of tensor trains using squared inverse Rosenblatt transports. Foundations of Computational Mathematics, 22, 1861-1922. [More Information]

2020

• Brown, R., Bardsley, J., Cui, T. (2020). Semivariogram methods for modeling Whittle–Matérn priors in Bayesian inverse problems. Inverse Problems, 36, 055006-1-055006-27. [More Information]

2017

• Bardsley, J., Cui, T. (2017). A Metropolis-Hastings-within-Gibbs sampler for nonlinear hierarchical-Bayesian inverse problems. In David R Wood, Jan de Gier, Cheryl E Praeger, Terence Tao (Eds.), 2017 MATRIX Annals, (pp. 1-10). Switzerland: Springer Nature Switzerland. [More Information]
• Wang, Z., Bardsley, J., Solonen, A., Cui, T., Marzouk, Y. (2017). Bayesian Inverse Problems with l-1 Priors: A Randomize-Then-Optimize Approach. SIAM Journal on Scientific Computing, 39(5), 5140-5166. [More Information]
• Spantini, A., Cui, T., Wilcox, K., Tenorio, L., Marzouk, Y. (2017). Goal-oriented optimal approximations of Bayesian linear inverse problems. SIAM Journal on Scientific Computing, 39(5), S167-S196. [More Information]
• Yee, N., Roosta-Khorasani, F., Cui, T. (2017). Optimization Methods for Inverse Problems. In David R Wood, Jan de Gier, Cheryl E Praeger, Terence Tao (Eds.), 2017 MATRIX Annals, (pp. 121-140). Switzerland: Springer Nature Switzerland.

2016

• Cui, T., Law, K., Marzouk, Y. (2016). Dimension-independent likelihood-informed MCMC. Journal of Computational Physics, 304, 109-137. [More Information]
• Peherstorfer, B., Cui, T., Marzouk, Y., Willcox, K. (2016). Multifidelity importance sampling. Computer Methods in Applied Mechanics and Engineering, 300, 490-509. [More Information]
• Solonen, A., Cui, T., Hakkarainen, J., Marzouk, Y. (2016). On dimension reduction in Gaussian filters. Inverse Problems, 32(4), 45003. [More Information]
• Cui, T., Ward, N., Eveson, S., Lähivaara, T. (2016). Pragmatic approach to calibrating distributed parameter groundwater models from pumping test data using adaptive delayed acceptance MCMC. Journal of Hydrologic Engineering, 21(2), 6015011. [More Information]
• Cui, T., Marzouk, Y., Willcox, K. (2016). Scalable posterior approximations for large-scale Bayesian inverse problems via likelihood-informed parameter and state reduction. Journal of Computational Physics, 315, 363-387. [More Information]

2015

• Cui, T., Marzouk, Y., Willcox, K. (2015). Data-driven model reduction for the Bayesian solution of inverse problems. International Journal for Numerical Methods in Engineering, 102(5), 966-990. [More Information]
• Spantini, A., Solonen, A., Cui, T., Martin, J., Tenorio, L., Marzouk, Y. (2015). Optimal low-rank approximations of Bayesian linear inverse problems. SIAM Journal on Scientific Computing, 37(6), A2451-A2487. [More Information]

2014

• Cui, T., Ward, N., Kaipio, J. (2014). Characterization of parameters for a spatially heterogenous aquifer from pumping test data. Journal of Hydrologic Engineering, 19(6), 1203-1213. [More Information]
• Cui, T., Martin, J., Marzouk, Y., Solonen, A., Spantini, A. (2014). Likelihood-informed dimension reduction for nonlinear inverse problems. Inverse Problems, 30(11), 114015. [More Information]

2013

• Cui, T., Ward, N. (2013). Uncertainty quantification for stream depletion tests. Journal of Hydrologic Engineering, 18(12), 1581-1590. [More Information]

2011

• Cui, T., Fox, C., O'Sullivan, M. (2011). Bayesian calibration of a large‐scale geothermal reservoir model by a new adaptive delayed acceptance Metropolis Hastings algorithm. Water Resources Research, 47(10), W10521-1-W10521-26. [More Information]