Primary Convener:
Alice-Agnes Gabriel, Scripps Institution of Oceanography

Convener:
Margarete Jadamec, University at Buffalo
Leif Karlstrom, University of Oregon
Mark Behn, Boston College

Chair:
Alice-Agnes Gabriel, Ludwig-Maximilians-Universität München
Margarete Jadamec, University at Buffalo
Leif Karlstrom, University of Oregon
Mark Behn, Boston College

Solid Earth hazards such as earthquakes, volcanic eruptions, and tsunami pose severe risks, necessitating enhanced modeling and mitigation strategies. Digital twins (DTs) - advanced computational frameworks combining near real-time data assimilation, multi-scale physics-based modeling, and machine learning - offer new capabilities for understanding and forecasting these complex hazards. This session explores cutting-edge developments in DT applications, emphasizing integration across observational, experimental, and computational geosciences. We invite presentations addressing physics-based modeling techniques, data-driven methodologies, uncertainty quantification strategies, and case studies demonstrating DT effectiveness in real-world hazard assessment. Contributions are encouraged from seismology, volcanology, hydrothermal systems, geodynamics, engineering, data science, and computational infrastructure, highlighting advancements and implications for societal resilience. We specifically welcome discussion on computational efficiency, scalability, and interdisciplinary collaboration enabled by High-Performance Computing workflows. DTs require the synthesis of observational data, multi-physics modeling techniques including the development of simplified surrogate models, and large-scale uncertainty quantification to achieve predictive breakthroughs.

Index Terms
1908 Cyberinfrastructure
1910 Data assimilation, integration and fusion
7290 Computational seismology
8410 Geochemical modeling

Cross-Listed:
T - Tectonophysics
IN - Informatics
V - Volcanology‚ Geochemistry and Petrology
S - Seismology

Suggested Itineraries:
Machine Learning and AI
Open Science and Open Data

Neighborhoods:
1. Science Nexus