RG: Machine Learning
Chairs
| Name | Affiliation | |
|---|---|---|
| 1. Hyunju Connor | NASA GSFC | [1] |
| 2. Matthew Argall | UNH | [2] |
| 3. Xiangning Chu | LASP, CU Boulder | [3] |
| 4. Bashi Ferdousi | AFRL | [4] |
| 5. Valluri Sai Gowtam | UAF | [5] |
About the ML-GEM Resource Group
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.
Goals and Objectives
1. Invite community-wide ML efforts: Extend invitations to a diverse community, including GEM, CEDAR, SHINE, and ML communities, fostering broader participation and collaboration.
2. Share ML advancements: Disseminate the latest ML techniques, including their advantages and disadvantages, and lessons learned. This knowledge-sharing will support community-wide geospace environment modeling efforts.
3. Facilitate integration discussions: Stimulate discussions on integrating diverse ML models across geospace systems, such as solar wind, magnetosheath, cusp, ring current, radiation belt, plasmasphere, and ionosphere.
4. Develop ML-based models: Initiate the development of an ML-based geospace environment model and encourage the strategic creation and inclusion of ML components in ML-GEM for enhanced performance.
5. Explore systematic responses: Investigate system-of-systems science in the solar wind – Earth interaction by leveraging ML-GEM and other ML models.
6. Compile a catalog of ML-ready dataset: Cleaning and preparing datasets constitute a substantial portion of machine learning model development. We will curate a list of existing ML-ready datasets in heliophysics for various scientific purposes.
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.
If you are interested in giving a talk in these sessions, please submit your talk at the following website: https://forms.gle/fmsU2eeBFFwD4tDQ8.
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).
Instructions for the ML-GEM Tutorial Session:
Welcome to the ML-GEM tutorial session! We will be using the Python programming language and the Google Colab platform for our exercises. There’s no need to download or install anything. Just follow these simple steps:
Step 1: Download the tutorial Jupyter notebook and the data files via the provided link. [Link to be determined soon]
Step 2: Sign in to your Google Drive account.
Step 3: Create a new folder named “LSTM_tutorial” in your Google Drive.
Step 4: Upload the Jupyter notebook and the data files into the “LSTM_tutorial” folder.
Step 5: Open the tutorial file by double-clicking on it. It will launch in the Google Colab platform.
Step 6: By default, the code will execute using the “CPU” option. To change this, locate the dropdown menu next to the “Connect” button in the top-right corner. Select “Change runtime type,” then choose the “T4 GPU” option.
You’re now fully prepared for the tutorial. If you run into any issues during the setup process, don’t hesitate to reach out. One of the co-chairs will be available to assist you. We’re excited to have you join us for the tutorial!
