RG: Machine Learning

Chairs

Hyunju Connor
Matthew Argall
Xiangning Chu
Bashi Ferdousi
Valluri Sai Gowtam

Contact:

[tbd]

[tbd]

Resource Group Formation

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

Machine Learning (ML) efforts have experienced rapid growth within the GEM community over the past few years. However, there has been no collective initiative to develop a new ML-based Geospace Environment Model (ML-GEM) that comprehensively connects ML-based models from the Sun to the solar wind, magnetosphere, and upper atmosphere to systematically understand and predict Sun – Earth interactions. Our new Focus Group (FG) aims to gather and lead diverse machine-learning efforts within the GEM community, with the primary goal of developing ML-GEM and gaining insights into our geospace systems from a data-driven perspective.

Over the next four years, our diverse FG team, consisting of four early-career scientists and two females, aims to unite ML efforts across the GEM community. We will actively explore innovative ways to link existing and developing ML models and establish the initial architecture of a new data-driven global geospace environment model. These efforts will subsequently lead to system-of-systems research to understand the collective behavior of geospace systems in response to incoming solar wind and Interplanetary Magnetic Field (IMF) conditions. Additionally, we will share new ML techniques and ML-ready dataset, outline the pros and cons of ML approaches, and offer valuable lessons learned, providing a resource for newcomers initiating their ML projects. The main research area of this FG is Global General Circulation Modeling (GGCM). However, our topics cover all other four GEM research areas, aligning well with all current GEM FGs.

Machine Learning (ML) efforts have experienced rapid growth within the GEM community over the past few years. However, there has been no collective initiative to develop a new ML-based Geospace Environment Model (ML-GEM) that comprehensively connects ML-based models from the Sun to the solar wind, magnetosphere, and upper atmosphere to systematically understand and predict Sun – Earth interactions. Our new Focus Group (FG) aims to gather and lead diverse machine-learning efforts within the GEM community, with the primary goal of developing ML-GEM and gaining insights into our geospace systems from a data-driven perspective.

Over the next four years, our diverse FG team, consisting of four early-career scientists and two females, aims to unite ML efforts across the GEM community. We will actively explore innovative ways to link existing and developing ML models and establish the initial architecture of a new data-driven global geospace environment model. These efforts will subsequently lead to system-of-systems research to understand the collective behavior of geospace systems in response to incoming solar wind and Interplanetary Magnetic Field (IMF) conditions. Additionally, we will share new ML techniques and ML-ready dataset, outline the pros and cons of ML approaches, and offer valuable lessons learned, providing a resource for newcomers initiating their ML projects. The main research area of this FG is Global General Circulation Modeling (GGCM). However, our topics cover all other four GEM research areas, aligning well with all current GEM FGs.

GEM-2024 Activities

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).