CEDAR email: Calls for Artificial Intelligence Studies on the ML-GEM Challenge Storms

[Connor, Hyunju K. (GSFC-6730)]

Dear CEDAR Colleagues,

Machine Learning-based Geospace Environment Modeling (ML-GEM) is a new resource group selected by the GEM Steering Committee. It has two primary goals: 1) to develop an Artificial Intelligence Modeling Framework for Advancing Heliophysics Research (AIMFAHR) by integrating community-wide AI efforts and 2) to advance system-of-systems science in Sun-Earth interaction from a data-driven perspective.

At the 2024 GEM summer workshop, ML-GEM participants selected three challenge storms to facilitate community-wide, AI-driven research activities over the coming years:

  1.  January 4, 2023 (minimum Sym-H :-74nT at 09:04UT)
  2.  May 6, 2023 (minimum Sym-H : -108 nT at 05:11 UT)
  3.  May 11, 2024 (minimum Sym-H : -518nT at 02:14 UT)

The goals of this initiative are to
1) Understand the current status of AI-driven heliophysics models,
2) Share new AI techniques and lessons learned,
3) Compile a catalog of ML-ready dataset, and
3) Unify community-wide AI efforts under a single modeling framework, AIMFAHR.

The ML-GEM resource group invites AI-driven studies on the challenge storms to a mini-GEM session, which will be scheduled in the afternoon of Sunday, December 8, prior to AGU, as well as to the regular ML-GEM session during the 2025 GEM-CEDAR summer workshop.

We look forward to your participation.
Hyunju Connor, Matthew Argall, Xiangning Chu, Bashi Ferdousi, and Valluri Sai Gowtam (ML-GEM chairs).


Note - If you receive this email on nights and/or weekends, that doesn't mean I expect you to read it or reply at that time. Please read & reply when you can during whatever your working hours happen to be.

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Hyunju Kim Connor (she/her/hers)
Research Astrophysicist
Geospace Physics Laboratory, Code 673
NASA Goddard Space Flight Center
Rm. 236, Bldg. 21
8800 Greenbelt RD, Greenbelt, MD 20771, USA
Email     : Hyunju.k.connor at nasa.gov<mailto:Hyunju.k.connor at nasa.gov>
Tel.         : 301-286-7417
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