Difference between revisions of "RG: Machine Learning"

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__TOC__
 
__TOC__
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==Chairs==
 
==Chairs==
  
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"""""" This page is under development """""
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{| class="wikitable"
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|+ style="caption-side:bottom; text-align:left | Table 1: RG Chairs.
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! Name          !! Affiliation !! Email
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|-
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| 1. Hyunju Connor  || NASA GSFC || [mailto:hyunju.k.connor@nasa.gov ]
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|-
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| 2. Matthew Argall  || UNH || [mailto:Matthew.Argall@unh.edu ]
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|-
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| 3. Xiangning Chu  || LASP, CU Boulder || [mailto:xiangning.chu@lasp.colorado.edu ]
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|-
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| 4. Bashi Ferdousi    || AFRL || [mailto:banafsheh.ferdousi@spaceforce.edu ]
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|-
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| 5. Valluri Sai Gowtam    || UAF || [mailto:svalluri@alaska.edu ]
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|}
  
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Hyunju Connor <br>
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==About the ML-GEM Resource Group==
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Matthew Argall <br>
 
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Xiangning Chu <br>
 
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Bashi Ferdousi <br>
 
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Valluri Sai Gowtam <br>
 
  
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Contact:  
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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.
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:[tbd]
 
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:[tbd]
 
  
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==Goals and Objectives==
  
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==Resource Group Formation==
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'''1. Invite community-wide ML efforts:''' Extend invitations to a diverse community, including GEM, CEDAR, SHINE, and ML communities, fostering broader participation and collaboration.
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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
 
  
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Machine Learning (ML) efforts have experienced rapid growth within the GEM community over
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'''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.
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the past few years. However, there has been no collective initiative to develop a new ML-based
 
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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.
 
  
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Over the next four years, our diverse FG team, consisting of four early-career scientists and
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'''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.
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two females, aims to unite ML efforts across the GEM community. We will actively explore
 
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innovative ways to link existing and developing ML models and establish the initial architecture
 
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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.
 
  
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Machine Learning (ML) efforts have experienced rapid growth within the GEM community over
+
'''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.
−
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
+
'''5. Explore systematic responses:''' Investigate system-of-systems science in the solar wind – Earth interaction by leveraging ML-GEM and other ML models.
−
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.
 
  
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==Goals and Objectives==
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'''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.
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Our overarching goals are to develop an ML-based geospace environment model and advance
 
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system-of-systems science in Sun-Earth interaction. Over the 4-year focus group period, we will
 
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implement the following objectives to achieve these goals:
 
−
 
 
−
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==
 
==GEM-2024 Activities==
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ML-GEM chairs have scheduled 4 sessions for the upcoming 2024 GEM Summer workshop held in Fort Collins, Colorado during June 23-28:
 
ML-GEM chairs have scheduled 4 sessions for the upcoming 2024 GEM Summer workshop held in Fort Collins, Colorado during June 23-28:
  
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1. ML-GEM stand-alone session :  All ML efforts across the GEM research areas are invited.
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'''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.  
  
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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.  
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'''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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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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'''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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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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<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.

Latest revision as of 13:57, 30 April 2024

Chairs

Table 1: RG Chairs.
Name Affiliation Email
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.

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 in these sessions, please submit your talk at the following website: https://forms.gle/fmsU2eeBFFwD4tDQ8.

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