Difference between revisions of "RG: Machine Learning"
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'''2. ML-GEM joint session with the Inner Magnetosphere Focus Groups :''' ML efforts particularly in the inner magnetosphere research area 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. | + | '''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. |
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| + | <u>'''If you are interested in giving a talk in these sessions, please submit your talk at the following website:'''</u> 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). | '''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). | ||
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Revision as of 12:40, 16 May 2024
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).
