Difference between revisions of "RG: Machine Learning"

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__TOC__
 
__TOC__
  
 +
==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.
  
−
==Chairs==
+
'''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.
  
−
"""""" This page is under development """""
+
Zoom Link: https://unh.zoom.us/j/93264767597?pwd=tjaJrNendpMdMGP8aaYbRKrIGnCVbO.1. Password: 435443
  
−
Hyunju Connor <br>
+
'''The discussion-only session with inner MAG focus groups.'''
−
Matthew Argall <br>
+
{| class="wikitable"
−
Xiangning Chu <br>
+
|+ style="caption-side:top; text-align:middle | MLGEM-RB-SCIMM-CP Joint Session: 3:30 - 5:00pm on Thursday, Jun 27
−
Bashi Ferdousi <br>
+
! !! Name !! Affiliation !! Title !! Time !! Topic
−
Valluri Sai Gowtam <br>
+
|-
 +
| 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. ||  ||
 +
|}
  
−
Contact:
 
−
:[tbd]
 
−
:[tbd]
 
  
 +
'''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.
  
−
==Resource Group Formation==
+
Zoom Link: https://unh.zoom.us/j/99110890924?pwd=LsvPTw5LtrTwQVeprH8uQZCbLp4DEF.1. Password: 034895
  
−
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
+
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).
  
−
Machine Learning (ML) efforts have experienced rapid growth within the GEM community over
+
{| class="wikitable"
−
the past few years. However, there has been no collective initiative to develop a new ML-based
+
|+ style="caption-side:top; text-align:middle | ML-GEM Standalone/Discussion Session: 10:30 - 12:00pm on Friday, Jun 28
−
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.
+
!  !! 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 ||
 +
|}
  
−
Over the next four years, our diverse FG team, consisting of four early-career scientists and
+
'''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).
−
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.
 
  
−
Machine Learning (ML) efforts have experienced rapid growth within the GEM community over
+
Zoom Link: https://unh.zoom.us/j/97969090948?pwd=KRF3SEW7E5GELUn8DXozlvTYbKsvMa.1. Password: 074062.
−
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
+
{| class="wikitable"
−
two females, aims to unite ML efforts across the GEM community. We will actively explore
+
|+ style="caption-side:top; text-align:middle | ML-GEM Tutorial Session: 1:30 - 3:00pm on Friday, Jun 28
−
innovative ways to link existing and developing ML models and establish the initial architecture
+
!  !! Name !! Affiliation !! Title !! Time !! Topic
−
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.
+
|-
 +
| 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 ||
 +
|}
  
−
==Goals and Objectives==
+
'''Instructions for the ML-GEM Tutorial Session:'''
  
−
Our overarching goals are to develop an ML-based geospace environment model and advance
+
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:
−
system-of-systems science in Sun-Earth interaction. Over the 4-year focus group period, we will
 
−
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.
+
'''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]
−
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==
+
'''Step 2:''' Sign in to your Google Drive account.
  
−
ML-GEM chairs have scheduled 4 sessions for the upcoming 2024 GEM Summer workshop held in Fort Collins, Colorado during June 23-28:
+
'''Step 3:''' Create a new folder named “LSTM_tutorial” in your Google Drive.
  
−
1. ML-GEM stand-alone session : All ML efforts across the GEM research areas are invited.
+
'''Step 4:''' Upload the Jupyter notebook and the data files into the “LSTM_tutorial” folder.
  
−
2. ML-GEM joint session with the Inner Magnetosphere Focus Groups : ML efforts particularly in the inner magnetosphere research area are invited.  
+
'''Step 5:''' Open the tutorial file by double-clicking on it. It will launch in the Google Colab platform.
  
−
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.  
+
'''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.
  
−
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).
+
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 10:56, 24 June 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 (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.

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.

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).

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 [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.

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. [7]

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!

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