Publications

1. Y. Liu and Giray Ökten, First Semester in Numerical Analysis with Python
2. Y. Liu and Giray Ökten, Probability and Simulation: A Python Companion, Springer, 2020; the Python companion can be accessed through the "Includes supplementary material: sn.pub/extras" link on the page.

1. Y. Li, Y. Liu, Y. Li, B. Hu, and P. Gai, Potential influences of leakage through a high permeability path on shallow aquifers in compressed air energy storage in aquifers, Renewable Energy, 209, 2023.
2. Y. Li, H. Yu, Y. Xiao, Y. Li, Y. Liu, X. Luo, D. Tang, G. Zhang and Y. Liu, Numerical verification on the feasibility of compressed carbon dioxide energy storage in two aquifers, Renewable Energy, 207, 2023.
3. Y. Li, H. Yu, Y. Li, X. Luo, Y. Liu, G. Zhang, D. Tang and Y. Liu, Full cycle modeling of inter-seasonal compressed air energy storage in aquifers , Energy, 263 Part D, 2023.
4. C. Dai, F. Ke, Y. Pan and Y. Liu, Exploring students’ learning support use in digital game-based math learning: A mixed-methods approach using machine learning and multi-cases study , Computers & Education, 194, 2023.
5. R. Sun, J. Dong, Y. Li, P. Li, Y. Liu, Y. Liu and J. Feng, The Influence Research on Nitrogen Transport and Reaction in the Hyporheic Zone with an In-Stream Structure , International Journal of Environmental Research and Public Health, 19, 2022.
6. Y. Li, Y. Li and Y. Liu, Numerical study on the impacts of layered heterogeneity on the underground process in compressed air energy storage in aquifers , Journal of Energy Storage, 46, 2022.
7. Y. Li, H. Yu, D. Tang, Y. Li, G. Zhang and Y. Liu, A comparison of compressed carbon dioxide energy storage and compressed air energy storage in aquifers using numerical methods , Renewable Energy, 187, 2022.
8. L. Fan, Y. Li, W. Luo, W. Qiao, W. Li, F. Liu, and Y. Liu, Evaluation of rural water supply sustainable operation and management based on cyclic correction framework – a case study of Chongqing, China , Water Supply, 22(2), 2021.
9. G. Ökten and Y. Liu, Randomized quasi-Monte Carlo methods in global sensitivity analysis , Reliability Engineering and System Safety, 210, 2021.
10. R. Chen, Y. Teng, H. Chen, W. Yue, X. Su, Y. Liu and Q. Zhang, A coupled optimization of groundwater remediation alternatives screening under health risk assessment: An application to a petroleum-contaminated site in a typical cold industrial region in Northeastern China , Journal of Hazardous Materials, 407, 2021.
11. Y. Li, Y. Liu, B. Hu, Y. Li, and J. Dong, Numerical investigation of a novel approach to coupling compressed air energy storage in aquifers with geothermal energy , Applied Energy, 279, 2020.
12. Y. Li, G. Zhang, H. Jiang, Y. Liu and C. Kuang, Performance assessment of a newly developed and highly stable sandy cementitious grout for karst aquifers in China , Environmental Earth Sciences, 79, 2020.
13. Y. Li, H. Yu, Y. Liu, G. Zhang, D. Tang and Z. Jiang, Numerical study on the hydrodynamic and thermodynamic properties of compressed carbon dioxide energy storage in aquifers , Renewable Energy, 151, 2020.
14. Y. Li, Y. Li, Y. Liu and X. Cao, Compressed air energy storage in aquifers: basic principles, considerable factors, and improvement approaches , Reviews in Chemical Engineering, 37(5), 2019.
15. J. Liu, Y. Li, G. Zhang, and Y. Liu, Effects of cementitious grout components on rheological properties , Construction and Building Materials, 227, 2019.
16. J. Sheng, T. Huang, Z. Ye, B. Hu, Y. Liu and Q. Fan, Evaluation of van Genuchten-Mualem model on the relative permeability for unsaturated flow in aperture-based fractures , Journal of Hydrology, 576, 2019.
17. Y. Liu, Efficient Bayesian Parameter Inversion Facilitated by Multi-Fidelity Modeling , Applied Computational Electromagnetics Society Journal, 34(2), 2019.
18. Q. He, H. Liu, Y. Hao, Y. Liu and W. Liu, Thermodynamic analysis of a novel supercritical compressed carbon dioxide energy storage system through advanced exergy analysis , Renewable Energy, 127, 2018.
19. Z. Ye, Q. Jiang, C. Yao, Y. Liu, A. Cheng, S. Huang and Y. Liu, The parabolic variational inequalities for variably-saturated water flow in heterogeneous fracture networks , Geofluids, 2018, 2018.
20. Y. Liu, G.S.H. Pau and S. Finsterle, Implicit sampling combined with reduced order modeling for the inversion of vadose zone hydrological data , Computers & Geosciences, 108, 2017.
21. A. Göncu, Y. Liu, G. Ökten and M.Y. Hussaini, Uncertainty and robustness in weather derivative models , Monte Carlo and Quasi-Monte Carlo Methods, 2016.
22. Y. Liu, M.Y. Hussaini and G. Ökten, Accurate construction of high dimensional model representation with applications to uncertainty quantification , Reliability Engineering & System Safety, 152, 2016.
23. G.S.H. Pau, C. Shen, W. Riley and Y. Liu, Accurate and efficient prediction of fine-resolution hydrologic and carbon dynamics from coarse-resolution models , Water Resources Research, 52, 2016.
24. Y. Zhang, Y. Liu, G.S.H. Pau, S. Oladyshkin and S. Finsterle, Evaluation of multiple reduced-order models to enhance confidence in global sensitivity analyses , International Journal of Greenhouse Gas Control, 49, 2016.
25. Y. Liu, G. Bisht, Z. M. Subin, W. J. Riley and G.S.H. Pau, A hybrid reduced-order model of fine-resolution hydrologic simulations at a polygonal tundra site , Vadose Zone Journal, 15(2), 2016.
26. Y. Liu, M.Y. Hussaini and G. Ökten, Global sensitivity analysis for the Rothermel model based on high dimensional model representation , Canadian Journal of Forest Research, 45, 2015.
27. J. Angela, Y. Liu, N. Cogan and M.Y. Hussaini, Global sensitivity analysis used to interpret biological experimental results , Journal of Mathematical Biology, 71, 2015.
28. Y. Liu, E. Jiménez, M.Y. Hussaini, G.Ökten and Scott Goodrick, Parametric uncertainty quantification in the Rothermel model with randomized quasi-Monte Carlo methods , International Journal of Wildland Fire, 24, 2015.
29. E. Jiménez, Y. Liu and M.Y. Hussaini, Variance reduction method based on sensitivity derivatives, Part 2 , Applied Numerical Mathematics, 74, 2013.
30. Y. Liu, M.Y. Hussaini and G. Ökten, Optimization of a Monte Carlo Variance Reduction Method Based on Sensitivity Derivatives , Applied Numerical Mathematics, 72, 2013.

1. Y. Liu and L. Zheng, Efficient reduced-order models for evaluating the impact of CO2 and brine leakage on groundwater , Proceedings of 2018 TOUGH Symposium, 2018.
2. Y. Liu Accurate and efficient Bayesian parameter inversion based on low-fidelity model solutions , International Applied Computational Electromagnetics Society Symposium, 2018.
3. Y. Liu, G.S.H. Pau and S. Finsterle, Bayesian parameter inversion with implicit sampling for a vadose zone hydrological model , Proceedings of TOUGH Symposium, 2015.
4. Y. Liu, M.Y. Hussaini and Giray Ökten, Global sensitivity analysis for the Rothermel model based on high dimensional model representation , 4th Fire Behavior and Fuels Conference Proceedings, 2013.