Post-Doctoral Fellow
Post-doctoral fellow
Active
Building 5, Level 3, 3204-WS02
Dr. Junjun Chen has been working as a Postdoctoral Fellow at KAUST, Saudi Arabia, since August 2025. He received his Ph.D. in Geological Resources and Geological Engineering from Jilin University, China, in 2023. From 2016 to 2018, Dr. Chen served as an Assistant Engineer at the Hydrogeological and Environmental Geological Survey Center of the China Geological Survey. From 2023 to 2025, he was an Associate Professor at the China University of Mining and Technology.
Dr. Chen’s current research interests focus on the development and application of machine learning methods for the forward and inverse modeling of nonlinear systems, monitoring design, and multi-objective optimization in related engineering problems. His research is primarily concerned with subsurface system studies, particularly multicomponent reactive solute transport simulation, groundwater contaminant plume migration, seawater intrusion, and geological carbon sequestration.
Chen, J., Dai, Z., Yang, Z., Pan, Y., Zhang, X., Wu, J., & Reza Soltanian, M. (2021). An improved tandem neural network architecture for inverse modeling of multicomponent reactive transport in porous media. Water Resources Research, 57, e2021WR030595. (Wiley top downloaded article, 2021 WRR Editor’s Choice Award)
Chen, J., Dai, Z., Dong, S., Zhang, X., Sun, G., Wu, J., Ershadnia, R., Yin, S., Soltanian, M.R., (2022). Integration of deep learning and information theory for designing monitoring networks in heterogeneous aquifer systems. Water Resources Research, 58, e2022WR032429. (AGU EOS Editor’s Highlight, 2022 WRR Editor’s Choice Award)
Cao, M., Dai, Z., Chen, J.*, Yin, H., Zhang, X., Wu, J., Thanh, H.O., Soltanian, M.R, (2025). An integrated framework of deep learning and entropy theory for enhanced high-dimensional permeability field identification in heterogeneous aquifers. Water Research, 2025, 268: 122706.
Ma, F., Chen, J.*, Dai, Z.*, Cai., Wang, D., Ma, Y, (2025). Impact of groundwater extraction intensity on the monitoring design for seawater intrusion in heterogeneous coastal aquifers. Journal of Hydrology, 661, 133638.
Chen, J., Dai, Z., Yin, S., Zhang, M., Soltanian, M.R., Enhancing Inverse Modeling in Groundwater Systems through Machine Learning: A Comprehensive Comparative Study. Hydrology and Earth System Sciences (Accepted)
Gan, Q., Song,H., Elsworth, D., Jia, S.*, Chen, J.*, Ma, F., Li, Q., Yang, Y., Wang, X., Dai, Z., Deep learning-enhanced global sensitivity analysis for uncertainty quantification in THMC coupled scCO2-EGS. Energy (Accepted)