Publications

You can also find my publications on my Google Scholar profile.

Peer Reviewed Papers

Reduced geostatistical approach with a Fourier neural operator surrogate model for inverse modeling of hydraulic tomography
Guo, Q., He, Y., Liu, M., Zhao, Y., Liu, Y., & Luo, J.
Water Resources Research, 59, doi: https://doi.org/10.1029/2023WR034939.
[paper] [code]

Predictive Deep Learning for High-Dimensional Inverse Modeling of Hydraulic Tomography in Gaussian and Non-Gaussian Fields
Guo, Q., Liu, M., & Luo, J. (2023)
Water Resources Research, 59, doi: https://doi.org/10.1029/2023WR035408.
[paper] [code]

High-dimensional inverse modeling of hydraulic tomography by physics informed neural network (HT-PINN)
Guo, Q., Zhao, Y., Lu, C., & Luo, J. (2023).
Journal of Hydrology, 616, 128828, doi: https://doi.org/10.1016/j.jhydrol.2022.128828.
[paper] [code]

High‐dimensional groundwater flow inverse modeling by upscaled effective model on principal components
Zhao, Y., Guo, Q., Lu, C., & Luo, J. (2022).
Water Resources Research, 58, doi: https://doi.org/10.1029/2022WR032610. [paper] [code]

Multiphysics modeling investigation of wellbore storage effect and non-Darcy flow
He, Y., Guo, Q., Liu, Y., Huang, H., Hou, D., & Luo, J. (2024).
Water Resources Research, 60, doi: https://doi.org/10.1029/2022WR032610. [paper] [data]

Contributed Presentations

Large-scale Inverse Modeling of Hydraulic Tomography by Physics Informed Neural Network
In: AGU Fall Meeting, Chicago, IL, December 2022
[abstract] [slides]

Physics informed neural network in groundwater inverse modeling In: Georgia Tech Water Resource Engineering Seminar, Atlanta, GA, March 2022. [slides]

Scalable high-dimensional inverse modeling of hydraulic tomography by physics informed neural network (HT-PINN)
In: National Environmental Conference for Doctoral Students, Beijing, China, January 2023 [slides]