Primary Convener:
Behzad Ghanbarian, University of Texas at Arlington

Convener:
Maruti Mudunuru, Pacific Northwest National Laboratory
Alexandre Tartakovsky, University of Illinois at Urbana Champaign
David Barajas-Solano, Pacific Northwest National Laboratory

Early Career Convener:
David Barajas-Solano, Pacific Northwest National Laboratory

Chair:
Behzad Ghanbarian, Kansas State University
Maruti Mudunuru, Pacific Northwest National Laboratory
Alexandre Tartakovsky, University of Illinois at Urbana Champaign
David Barajas-Solano, Pacific Northwest National Laboratory

The integration of artificial intelligence (AI) and data science into subsurface hydrology and energy is transforming our capabilities to model and manage complex processes. This session invites innovative applications of AI, including machine learning, deep learning, and large language models, to groundwater flow, contaminant transport, geothermal energy, carbon sequestration, underground hydrogen storage, and hydrocarbon recovery. This session aims to address challenges related to data scarcity, data diversity, AI-based model uncertainty, surrogate models, and multi-scale heterogeneity, as well as interdisciplinary efforts that combine AI with physics-based models, laboratory measurements, and field data. We encourage contributions that discuss interpretability, robustness, and the deployment of AI in decision-making for sustainable energy and subsurface water resources. This session offers a transformative opportunity for hydrologists, hydrogeologists, geoscientists, energy researchers, and data scientists to foster transdisciplinary collaborations, facilitate knowledge exchange, and advance the frontiers of AI in subsurface studies.

Index Terms
1402 - Critical Zone
1832 Groundwater transport
5104 Fracture and flow
5139 Transport properties

Cross-Listed:
NG - Nonlinear Geophysics
NS - Near Surface Geophysics
S - Seismology
EP - Earth and Planetary Surface Processes

Co-Sponsored Sessions:
CGS： Chinese Geophysical Society
CGU： Canadian Geophysical Union
GSA： Geological Society of America
EGU： European Geosciences Union

Suggested Itineraries:
Machine Learning and AI
Critical Minerals and Renewable Energy

Neighborhoods:
3. Earth Covering