Machine Learning based Geospace Environment Modeling (ML-GEM) is a new resource group selected by the GEM Steering Committee, with two primary goals: advancing system-of-systems science in Sun-Earth interaction from a data-driven perspective and developing an ML-based Geospace Environment Modeling by integrating community-wide ML efforts.    

ML-GEM chairs have scheduled 4 sessions for the upcoming 2024 GEM Summer workshop held in Fort Collins, Colorado during June 23-28:

1. ML-GEM stand-alone session :  All ML efforts across the GEM research areas are invited.

2. ML-GEM joint session with the Inner Magnetosphere Focus Groups : ML efforts particularly in the inner magnetosphere research area are invited. 

3. ML-GEM discussion session : Please submit a summary of your ML model (1-2 slides) — the slide format to be announced — and join the discussion on how to integrate your model into a unified, data-driven geospace environment model.   

4. ML-GEM tutorial session:  A hands-on tutorial on the Long Short-Term Memory (LSTM) technique that models time-series data. This tutorial will use the LSTM model and the SuperMAG geomagnetic field data, published in Blandin et al. (2022; https://doi.org/10.3389/fspas.2022.846291). 

If you are interested in giving a talk, please submit the following information.

ML-GEM Chairs: Hyunju Connor, Matthew Argall, Xiangning Chu, Bashi Ferdousi, Valluri Sai Gowtam.