Subsurface energy and environmental systems like geothermal energy systems, geological carbon storage, underground hydrogen storage, groundwater flow systems, etc., play a vital role for mitigating climate change and achieving net-zero. At the same time, their complex, heterogeneous, multiscale, and uncertain nature presents major challenges for modelling, prediction, monitoring, and decision-making, particularly in multiphysics and multiscale model coupling, data integration, and real-time monitoring and control.
Artificial intelligence (AI) and scientific machine learning (SciML) are creating new opportunities to address these challenges. This session aims to bring together researchers from geoscience, hydrology, subsurface energy, computational science, and AI to discuss recent advances at the interface between these fields. We invite contributions on the development and application of AI and SciML methods for subsurface energy and environmental systems. Topics include, but are not limited to, physics-informed machine learning, neural operators, reduced-order modelling, surrogate and generative modelling, multimodal data fusion, digital twins, explainable and trustworthy AI, large language models, and agentic AI.
The session will provide a platform for interdisciplinary exchange on emerging methods, practical applications, current limitations, and future research directions.