• Functional areas
  • Machine learning (both training and inference) at scale both for big models as well as massively parallel training and inference of many models
  • Supporting both individual researchers training and deploying their own models, as well as centralized production workflows using machine learning at scale
  • Fast inference and training
  • Calibration/validation
• Mandate
  • Describe the machine learning training and inference needs of the stakeholders
  • What are the resources needed to execute these workflows?
  • What is the technology evolution of these resources?
    • Coordinate with experimental algorithms and theoretical calculations and simulations
  • How will the stakeholders be able to design and write machine learning applications for these resources
  • Are there standards that the community should follow?
  • How are the solutions used by the community embedded/derived from solutions from industry/other science domains

The general time schedule can be found here: https://snowmass21.org/computational/start#time_schedule

* Meetings

• On zoom: https://mit.zoom.us/j/92827341251 as needed
• The date and time of future meetings will be announced via email and slack
• Mailing List instructions Our list: [email protected]
• Slack instructions (very end of page)

* Nov. 2020 - Dec. 2020

• Organize submitted LOIs into white papers
• Nov 10: Meeting on Physics-specific ML: Slides
• Nov 24: Meeting on Interpretation and uncertainty:Slides
• Dec 14: Meeting on remaining themes

Here is the list of submitted LOIs to this topical group. First index before “/” corresponds to the primary frontier used for the submission.