Difference between revisions of "RG: Machine Learning"
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− | """ | + | ==Chairs== |
+ | |||
+ | {| class="wikitable" | ||
+ | |+ style="caption-side:bottom; text-align:left | Table 1: RG Chairs. | ||
+ | ! Name !! Affiliation !! Email | ||
+ | |- | ||
+ | | 1. Hyunju Connor || NASA GSFC || [mailto:hyunju.k.connor@nasa.gov ] | ||
+ | |- | ||
+ | | 2. Matthew Argall || UNH || [mailto:Matthew.Argall@unh.edu ] | ||
+ | |- | ||
+ | | 3. Xiangning Chu || LASP, CU Boulder || [mailto:xiangning.chu@lasp.colorado.edu ] | ||
+ | |- | ||
+ | | 4. Bashi Ferdousi || AFRL || [mailto:banafsheh.ferdousi@spaceforce.edu ] | ||
+ | |- | ||
+ | | 5. Valluri Sai Gowtam || UAF || [mailto:svalluri@alaska.edu ] | ||
+ | |} | ||
+ | |||
+ | ==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 (10:30am - 12:00pm on 06/27 Thursday):''' All ML efforts across the GEM research areas are invited. | |
− | |||
− | |||
− | |||
− | |||
− | + | Zoom Link: https://unh.zoom.us/j/92641406400?pwd=ghwL2kuHUr4ZUCcZZVd8rVFk1Q5XGn.1, Password: 452277. | |
− | : | ||
− | : | ||
+ | '''8min/presentation.''' We recommend 6min presentation, 1min question, and 1min transition. | ||
− | == | + | {| class="wikitable" |
+ | |+ style="caption-side:top; text-align:middle | ML-GEM Standalone Session Schedule: 10:30am - 12:00pm on Thursday, Jun 27 | ||
+ | ! !! 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 | ||
+ | |- | ||
+ | | 12 || Drew Turner || APL/JHU || Unsupervised clustering and a generative model for Earth's bow shock using MMS data || 11:58 || Dayside | ||
+ | |} | ||
− | + | '''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. | |
− | + | Zoom Link: https://unh.zoom.us/j/93264767597?pwd=tjaJrNendpMdMGP8aaYbRKrIGnCVbO.1. Password: 435443 | |
− | |||
− | |||
− | + | '''The discussion-only session with inner MAG focus groups.''' | |
− | + | {| class="wikitable" | |
− | + | |+ style="caption-side:top; text-align:middle | MLGEM-RB-SCIMM-CP Joint Session: 3:30 - 5:00pm on Thursday, Jun 27 | |
− | + | ! !! Name !! Affiliation !! Title !! Time !! Topic | |
+ | |- | ||
+ | | 1 || FG/RG Chairs || || Science questions for MLGEM, RB, SCIMM, and CP fields || || | ||
+ | |- | ||
+ | | 2 || Everyone || || Discussion || || | ||
+ | |- | ||
+ | | || || || - Identify an open question, pair ML and data analysis, 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. | |
− | |||
− | |||
− | |||
− | = | + | Zoom Link: https://unh.zoom.us/j/99110890924?pwd=LsvPTw5LtrTwQVeprH8uQZCbLp4DEF.1. Password: 034895 |
− | + | The session will host three talks from the standalone session (10:30 to 10:54am) and then move to the discussion sessions (10:54am -12:00pm). | |
− | |||
− | |||
− | + | {| class="wikitable" | |
+ | |+ style="caption-side:top; text-align:middle | ML-GEM Standalone/Discussion Session: 10:30 - 12:00pm on Friday, Jun 28 | ||
+ | ! !! Name !! Affiliation !! Title !! Time !! Topic | ||
+ | |- | ||
+ | | 1 || Jacob Bortnik || UCLA || Using interpretable AI to discover the drivers of acceleration vs depletion radiation belt events || 10:30 || Inner Mag | ||
+ | |- | ||
+ | | 2 || Qusai Al Shidi || WVU || Reduced Order Probabilistic Emulator of RAM-SCB: Towards Non-linearity with Deep Learning || 10:38 || Inner Mag | ||
+ | |- | ||
+ | | 3 || Man Hua || UCLA || Machine‐learning based identification of the critical driving factors controlling storm‐time outer radiation belt electron flux dropouts || 10:46 || Inner Mag | ||
+ | |- | ||
+ | | 4 || Hyunju Connor || NASA/GSFC || Overview of ML-GEL goals, plan, and discussion topics || 10:54 || | ||
+ | |- | ||
+ | | 5 || '''Everyone''' || || '''Presentation of current ML models (2min/model) : Submit slide format''' [https://docs.google.com/presentation/d/1qzgn880hCaDHLkEhL6GUEcEI3m5SJjQK/edit#slide=id.g2e728881d58_0_266] || 11:00 || | ||
+ | |- | ||
+ | | || || || '''Discussion''' || || | ||
+ | |- | ||
+ | | || || || 1. Integrate the ML models into a unified AI framework of Heliophysics Modeling || | ||
+ | |- | ||
+ | | || || || 2. Select a common testing dataset (year, storms, substorms) for the framework validation || | ||
+ | |- | ||
+ | | || || || 3. Determine action items for the next ML-GEM meeting || | ||
+ | |} | ||
− | + | '''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). | |
− | + | Zoom Link: https://unh.zoom.us/j/97969090948?pwd=KRF3SEW7E5GELUn8DXozlvTYbKsvMa.1. Password: 074062. | |
− | + | {| class="wikitable" | |
+ | |+ style="caption-side:top; text-align:middle | ML-GEM Tutorial Session: 1:30 - 3:00pm on Friday, Jun 28 | ||
+ | ! !! Name !! Affiliation !! Title !! Time !! Topic | ||
+ | |- | ||
+ | | 1 || Hyunju Connor || NASA/GSFC || Long Short-TERM Memory Model of Alaska geomagnetic field variation || 1:30 || | ||
+ | |- | ||
+ | | 2 || Matt Blandin || UAF || Introduction of the LSTM code || 1:40 || | ||
+ | |- | ||
+ | | 3 || Everyone || - || Hands-on Activities: Follow the '''instructions below''' to get started. || 1:50 || | ||
+ | |} | ||
− | + | '''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:https://drive.google.com/drive/u/1/folders/1CflXlSi5KZ4Iy0f4MkWRrR_j8t6mmLO1 | |
− | + | '''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. | |
− | If you | + | 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! |
Latest revision as of 08:38, 27 June 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 (10:30am - 12:00pm on 06/27 Thursday): All ML efforts across the GEM research areas are invited.
Zoom Link: https://unh.zoom.us/j/92641406400?pwd=ghwL2kuHUr4ZUCcZZVd8rVFk1Q5XGn.1, Password: 452277.
8min/presentation. We recommend 6min presentation, 1min question, and 1min transition.
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 |
12 | Drew Turner | APL/JHU | Unsupervised clustering and a generative model for Earth's bow shock using MMS data | 11:58 | Dayside |
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.
Zoom Link: https://unh.zoom.us/j/93264767597?pwd=tjaJrNendpMdMGP8aaYbRKrIGnCVbO.1. Password: 435443
The discussion-only session with inner MAG focus groups.
Name | Affiliation | Title | Time | Topic | |
---|---|---|---|---|---|
1 | FG/RG Chairs | Science questions for MLGEM, RB, SCIMM, and CP fields | |||
2 | Everyone | Discussion | |||
- Identify an open question, pair ML and data analysis, 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.
Zoom Link: https://unh.zoom.us/j/99110890924?pwd=LsvPTw5LtrTwQVeprH8uQZCbLp4DEF.1. Password: 034895
The session will host three talks from the standalone session (10:30 to 10:54am) and then move to the discussion sessions (10:54am -12:00pm).
Name | Affiliation | Title | Time | Topic | |
---|---|---|---|---|---|
1 | Jacob Bortnik | UCLA | Using interpretable AI to discover the drivers of acceleration vs depletion radiation belt events | 10:30 | Inner Mag |
2 | Qusai Al Shidi | WVU | Reduced Order Probabilistic Emulator of RAM-SCB: Towards Non-linearity with Deep Learning | 10:38 | Inner Mag |
3 | Man Hua | UCLA | Machine‐learning based identification of the critical driving factors controlling storm‐time outer radiation belt electron flux dropouts | 10:46 | Inner Mag |
4 | Hyunju Connor | NASA/GSFC | Overview of ML-GEL goals, plan, and discussion topics | 10:54 | |
5 | Everyone | Presentation of current ML models (2min/model) : Submit slide format [6] | 11:00 | ||
Discussion | |||||
1. Integrate the ML models into a unified AI framework of Heliophysics Modeling | |||||
2. Select a common testing dataset (year, storms, substorms) for the framework validation | |||||
3. Determine action items for the next ML-GEM meeting |
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).
Zoom Link: https://unh.zoom.us/j/97969090948?pwd=KRF3SEW7E5GELUn8DXozlvTYbKsvMa.1. Password: 074062.
Name | Affiliation | Title | Time | Topic | |
---|---|---|---|---|---|
1 | Hyunju Connor | NASA/GSFC | Long Short-TERM Memory Model of Alaska geomagnetic field variation | 1:30 | |
2 | Matt Blandin | UAF | Introduction of the LSTM code | 1:40 | |
3 | Everyone | - | Hands-on Activities: Follow the instructions below to get started. | 1:50 |
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:https://drive.google.com/drive/u/1/folders/1CflXlSi5KZ4Iy0f4MkWRrR_j8t6mmLO1
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!