1.• Triantafyllidis, V., Xing, W., Shah, A. A., & Nair, P. B. (2016). Neural network emulation of spatio-temporal data using linear and nonlinear dimensionality reduction. In Advanced Computer and Communication Engineering Technology (pp. 1015-1029). Springer, Cham.

2.• Xing, W.W., Triantafyllidis, V., Shah, A.A., Nair, P.B. and Zabaras, N., 2016. Manifold learning for the emulation of spatial fields from computational models. Journal of Computational Physics, 326, pp.666-690. （中科院分区Q2，JCR Q1）

3.• Shah, A. A., Xing, W. W., & Triantafyllidis, V. (2017). Reduced-order modelling of parameter-dependent, linear and nonlinear dynamic partial differential equation models. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 473(2200), 2016080...

4.• Xing, W., Shah, A. A., Urasinska-Wojcik, B., & Gardner, J. W. (2017, October). Prediction of impurities in hydrogen fuel supplies using a thermally-modulated CMOS gas sensor: Experiments and modelling. In 2017 IEEE SENSORS (pp. 1-3). IEEE.

5.• Triantafyllidiis, V., Xing, W. W., Leung, P. K., Rodchanarowan, A., & Shah, A. A. (2018, June). Probabilistic sensitivity analysis for multivariate model outputs with applications to Li-ion batteries. In Journal of Physics: Conference Series (Vol. 1039, No. 1, p. 012020). I...

6.• Gadd, C., Xing, W., Nezhad, M. M., & Shah, A. A. (2019). A surrogate modelling approach based on nonlinear dimension reduction for uncertainty quantification in groundwater flow models. Transport in porous media, 126(1), 39-77. （中科院分区Q2，JCR Q2）