Physics Community Needs, Tools, and Resources for Machine Learning
Philip Harris, Erik Katsavounidis, William Patrick McCormack, Dylan, Rankin, Yongbin Feng, Abhijith Gandrakota, Christian Herwig, Burt Holzman,, Kevin Pedro, Nhan Tran, Tingjun Yang, Jennifer Ngadiuba, Michael Coughlin,, Scott Hauck, Shih-Chieh Hsu, Elham E Khoda, Deming Chen
TL;DR
This paper discusses the growing importance of machine learning in physics research, highlighting community needs, available tools, and resources to address computational challenges and enhance research capabilities.
Contribution
It provides a comprehensive overview of the current state and future directions for ML tools and resources tailored to physics research needs.
Findings
Identifies key ML needs across different physics research regimes
Reviews existing tools and resources for physics-related ML
Suggests strategies for effective utilization and access to ML resources
Abstract
Machine learning (ML) is becoming an increasingly important component of cutting-edge physics research, but its computational requirements present significant challenges. In this white paper, we discuss the needs of the physics community regarding ML across latency and throughput regimes, the tools and resources that offer the possibility of addressing these needs, and how these can be best utilized and accessed in the coming years.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsScientific Computing and Data Management · Quantum Computing Algorithms and Architecture · Machine Learning in Materials Science