Dear Colleagues,
The Machine Learning-based Geospace Environment Modeling (MLGEM) resource group is excited to announce one standalone session and two joint sessions at the upcoming GEM 2026 Workshop, which will be held from from July 12-17 in Portland, ME at the Holiday Inn By the Bay.
Interested speakers are invited to submit their presentation titles by July 6 by completing this form.
The session schedule and descriptions are provided below.
Session 1: Standalone MLGEM Session - General Contributions
July 13, 2026 (Monday, 10:30 AM – 12:00 PM)This session will provide a dedicated forum for researchers to present the latest advances in machine learning applications for geospace and environmental modeling. We encourage submissions covering novel methodologies, scientific discoveries enabled by AI/ML, operational nowcast and forecast models, foundation models, uncertainty quantification, and other emerging machine learning technologies relevant to geospace science.
Session 2: MLGEM/MPEC/GIC/MAC Joint Session - Presentations
July 13, 2026 (Monday, 1:30 PM – 3:00 PM)This joint session will highlight machine learning research relevant to the Magnetosphere–Ionosphere Coupling community. Topics may include the use of AI/ML techniques to investigate coupling processes, improve event detection and prediction, analyze large and complex datasets, enhance scientific understanding, and uncover new insights into the coupled geospace system.
Session 3: MLGEM/MPEC/GIC/MAC Joint Session - Discussions
July 15, 2026 (Wednesday, 1:30 PM – 3:00 PM)This interactive discussion session will focus on how artificial intelligence, data analysis, and physics-based modeling interact to support and advance magnetosphere–ionosphere coupling research. We invite modelers, data scientists, and researchers to share perspectives on current challenges and future opportunities.Discussion topics include:
- ML-based empirical formulations to support geospace modeling
- Validation and benchmarking of physics-based models with data-driven approaches
- Opportunities for community collaboration and shared resources for improving old empirical models/formulations used in physics-based models
For colleagues who are unable to attend the workshop in person, a virtual presentation option will be available.
We look forward to your contributions and to engaging discussions at GEM 2026.
Best regards,
MLGEM Team.
