RG: Machine Learning

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.

If you are interested in giving a talk in these sessions, please submit your talk at the following website: https://forms.gle/fmsU2eeBFFwD4tDQ8.

1. ML-GEM stand-alone session (10:30am - 12:00pm on 06/27 Thursday): All ML efforts across the GEM research areas are invited.

Table 2: Schedule For standalone session.

	Name	Affiliation	Title	Time	Topic
1	Mikhail Sitnov	APL/JHU	Data mining of the cislunar magnetotail	10:30	Tail/nightside
2	Savvas Raptis	APL/JHU	Modeling Earth's Plasma Sheet with Machine learning	10:38	Tail/nightside
3	Xiaofei Shi	UCLA	Data mining-based reconstruction of the transition region in the nightside magnetosphere validated through ELFIN isotropic boundaries	10:46	Tail/nightside
4	James Edmond/Jimmy Raeder	UNH	Clustering of Plasma Region Observations using Self-Organizing Maps	10:54	Dayside
5	Connor O'Brien	BU	PRIME-SH: A Data-Driven Probabilistic Model of Earth’s Magnetosheath	11:02	Dayside
6	Sai Gowtam Valluri	UAF	Machine Learning-based Particle Precipitation Model (ML-PPM)	11:10	Ionosphere
7	Matthew Blandin	UAF	Residual Convolutional Neural Networks for Global Geomagnetic Field Predictions	11:18	GIC
8	Jonathan Mellina	LASP	Tidal Effects in Earth's Plasmasphere Using a Machine Learning Model	11:26	Plasmasphere
9	Wen Li	BU	Integrate Deep Learning into Physics-Based Simulation: Modeling Global Electron Precipitation Driven by Whistler Mode Waves	11:34	Inner Mag
10	Evan McPherson	LASP	Imbalanced Regressive Model of Electron Fluxes in the Earth's Outer Radiation Belt	11:42	Inner Mag
11	Xiangning Chu	LASP	Imbalanced regressive neural network model for whistler-mode hiss waves: spatial and temporal evolution	11:50	Inner Mag

2. ML-GEM joint session with the Inner Magnetosphere Focus Groups (3:30 - 05:00 PM on 06/27 Thursday): ML efforts particularly in the inner magnetosphere research area are invited. We will discuss how to align ML efforts with current science associated with the SCIMM/RB/CP Focus Groups. An outcome will be to identify an open question, pair ML and data analysts, and create a plan to pursue answers for the next GEM Workshop.

3. ML-GEM discussion session (10:30am - 12:00pm on 06/28 Friday): We will showcase currently available AI models in the heliophysics community and discuss how to unify them into a single AI modeling framework for understanding and predicting the geospace environment. Please submit a 2-page summary of your AI model at https://docs.google.com/presentation/d/1qzgn880hCaDHLkEhL6GUEcEI3m5SJjQK/edit?usp=drive_link&ouid=115923735998882159942&rtpof=true&sd=true

4. ML-GEM tutorial session (1:30 - 3:00pm on 06/28 Friday): 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!