Physics Community Needs, Tools, and Resources for Machine Learning
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 36 of its 161 references that are in the map. First posted on arXiv 30 Mar 2022.
Builds on
36 of 161 references.
References · 36
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24 more
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Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 0; 2026: 1.
2 papers.