From xiyuanbao at g.ucla.edu Wed Jul 8 05:33:04 2026 From: xiyuanbao at g.ucla.edu (XIYUAN BAO) Date: Tue, 7 Jul 2026 15:33:04 -0400 Subject: [Geodynamics] =?utf-8?q?AGU_26_Call_for_Abstracts=C2=A0in_Sessio?= =?utf-8?q?n_DI001_-_Advances_in_Machine_Learning_for_Solid_Earth_G?= =?utf-8?q?eoscience?= Message-ID: Dear colleagues, Please consider submitting an abstract to our AGU 2026 session: DI001 - Advances in Machine Learning for Solid Earth Geoscience. With the expansion of observational and experimental datasets, advances in machine learning methods, and growing computational capabilities, machine learning is becoming an increasingly important component of Solid Earth geoscience. These approaches are opening new avenues for investigating physical and chemical processes across spatial and temporal scales, from the surface to the deep interior, on Earth and other terrestrial planetary bodies. This session welcomes contributions spanning a wide range of methods and applications, including data compilation and mining, statistical learning, classical and deep neural networks, explainable AI, generative models, and related approaches applied to geophysics, geodynamics, geochemistry, structural geology, volcanology, petrology, mineralogy, and mineral physics. We encourage both methodological developments tailored to geoscientific problems and application-focused studies that yield new insights into Solid Earth processes. Example topics include data mining of geochemical, mineralogical, or volcanological datasets; deep learning-based geophysical inversion; physics-informed emulators for geodynamics and landscape evolution; and machine-learning-assisted multiscale modeling. The deadline to submit your abstract is Wednesday, 5 August 2026, 23:59 PM EDT. Invited Speakers: Mostafa Mousavi, Harvard University Daniel O'Malley, Los Alamos National Laboratory On behalf of conveners, Xiyuan Bao, Weiqiang Zhu, Shaunna Morrison and Tushar Mittal -------------- next part -------------- An HTML attachment was scrubbed... URL: