CEDAR email: Invitation to Participate at MLGEM Sessions - GEM 2026

[Sai Gowtam V]

Dear Colleagues,



The Machine Learning-based Geospace Environment Modeling (MLGEM) resource
group is excited to announce one standalone session for general
contributions and two joint sessions exploring magnetosphere-ionosphere
coupling research at the upcoming GEM 2026 Workshop, which will be held
from July 12-17 in Portland, ME, at the Holiday Inn By the Bay.


Interested speakers are invited to submit presentation titles by July 6 using
the following link:

https://forms.gle/EqMbQw33wz1NrmyL7.


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  - Discussion

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,

Gowtam Valluri, Hyunju Connor, Bashi Ferdousi, Xiangning Chu, Matt Argall.
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