[1] X. Zhang, S. Jiang*, N. Zheng, X. Xia, Z. Li, R. Zhang, and X. Wang, Integration of DDPM and ILUES for Simultaneous Identification of Contaminant Source Parameters and Non-Gaussian Channelized Hydraulic Conductivity Field, Water Resources Research, 2024, https://doi.org/10.1029/2023WR036893.

[2] X. Zhang, S. Jiang*, J. Wei, Chao Wu, X. Wang, X. Xia, N. Zheng, J. Xing, Non-Gaussian Hydraulic Conductivity and Potential Contaminant Source Identification: A Comparison of Two Advanced DLPM-based Inversion Framework, Journal of Hydrology, 2024, https://doi.org/10.1016/j.jhydrol.2024.131540.

[3] M. Guo, N. Zhou*, Y. Cai, X. Wang, X. Zhang, S. Lu, K. Liu, W. Zhao, Quantitative estimation of vegetation carbon source/sink and its response to climate variability and anthropogenic activities in Dongting Lake Wetland, China, Remote Sensing, 2025, https://doi.org/10.3390/rs17142475.

[4] J. Wei, S. Jiang*, Q. Liu, X. Zhang, N. Zheng, J. Xing, Toward Closed-Loop Remediation: A Dynamic Optimization Approach for Hydraulic Conductivity Estimation and Pump-and-Treat Design, Journal of Contaminant Hydrology, 2026, https://doi.org/10.1016/j.jconhyd.2026.104852.

[5] X. Zhang, W. Yang, J. Zhang, S. Jiang*, Diffusion-Inversion-Net (DIN): An End-to-End Direct Probabilistic Framework for Characterizing Hydraulic Conductivities and Quantifying Uncertainty, Journal of Hydrology, Accepted, 2026, https://arxiv.org/abs/2511.16926.

[6] B. Zhang, F. Wang*, X. Zhang, Field characteristics and initiation mechanisms of debris floods: Insights from western Beijing, China, Geomorphology, Accepted, 2026, https://doi.org/10.1016/j.geomorph.2026.110584.

[7] W. Yang, X. Zhang*, S. Jiang*, PIS: A Generalized Physical Inversion Solver for Arbitrary Sparse Observations via Set Conditioned Flow Matching, Submitted to Knowledge-Based Systems, 2026, https://arxiv.org/abs/2512.13732.

[8] W. Yang, X. Zhang*, S. Jiang*, Dynamic Operator-Guided Flow Matching: A Generative Physical Inverse Solver for Arbitrary Sparse Observations, Submitted to Machine Learning: Science and Technology, 2026.

[9] W. Yang, X. Zhang*, S. Jiang*, Beyond L₂: Sobolev-Kinematic Flow Matching for Extreme Sparse Physical Inversion, Submitted to NIPS 2026.

[10] Yang, Q., Qiu, S., Li, B., Shan, X., Feng, J., Zhou, S., Zhou, X., Xing, T., Guo, J., Dong, X., Liu, G., Liu, X., Pu, H., Deng, Q., X. Zhang, Xiang, Z., Qian, H., Yan, Y., Xu, Y., Lei, N., Jia, T., Shan, B., & De Michele, C., HydroAgent: Formalizing Forecaster Expertise into Skill-Orchestrated Flood Forecasting Workflows. Submitted to AGU Advances, 2026.

[1] The 35th KKHTCNN Symposium on Civil Engineering (Nov. 2024): X. Zhang (presenter), S. Jiang*, Non-Gaussian hydraulic conductivity and potential contaminant source identification: A comparison of two advanced DLPM-based inversion framework. Oral presentation, Track 5, Shanghai, China.

[2] International Forum on Groundwater (Aug. 2024): X. Zhang (presenter), S. Jiang*, Leveraging Deep Generative Models for Enhanced Hydraulic Conductivity Estimation in Groundwater Modeling. Oral presentation, Session 14, Jilin, China. Outstanding Graduate Student Presentation Award.

[3] The 9th Young Scholars' Forum in Groundwater Science (Nov. 2025): X. Zhang (presenter), S. Jiang*, Prospects of Deep Generative Models in Groundwater Parameter Inversion: From Parameterization to Direct Inversion Modeling. Oral presentation, Session 3, Hefei, China.

[4] International Conference on Applied Energy (May 2026): X. Zhang, W. Yang, S. Jiang*, Q. Zheng*, Diffusion-Inversion-Net (DIN): An End-to-End Direct Probabilistic Framework for Characterizing Hydraulic Conductivities and Quantifying Uncertainty, Ningbo, China.