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Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning
- Source :
- INSPIRE-HEP
-
Abstract
- The rapidly-developing intersection of machine learning (ML) with high-energy physics (HEP) presents both opportunities and challenges to our community. Far beyond applications of standard ML tools to HEP problems, genuinely new and potentially revolutionary approaches are being developed by a generation of talent literate in both fields. There is an urgent need to support the needs of the interdisciplinary community driving these developments, including funding dedicated research at the intersection of the two fields, investing in high-performance computing at universities and tailoring allocation policies to support this work, developing of community tools and standards, and providing education and career paths for young researchers attracted by the intellectual vitality of machine learning for high energy physics.<br />Comment: Contribution to Snowmass 2021
- Subjects :
- High Energy Physics - Theory
FOS: Computer and information sciences
Computer Science - Artificial Intelligence
Other Fields of Physics
FOS: Physical sciences
hep-lat
programming
High Energy Physics - Experiment
High Energy Physics - Experiment (hep-ex)
High Energy Physics - Lattice
[PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex]
[INFO]Computer Science [cs]
activity report
[PHYS.HLAT]Physics [physics]/High Energy Physics - Lattice [hep-lat]
[PHYS.HTHE]Physics [physics]/High Energy Physics - Theory [hep-th]
hep-ex
hep-th
High Energy Physics - Lattice (hep-lat)
Particle Physics - Lattice
Computational Physics (physics.comp-ph)
cs.AI
Computing and Computers
machine learning
Artificial Intelligence (cs.AI)
High Energy Physics - Theory (hep-th)
physics.comp-ph
Physics - Computational Physics
Particle Physics - Theory
Particle Physics - Experiment
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- INSPIRE-HEP
- Accession number :
- edsair.doi.dedup.....881b4d8239ca61bf3681e47ddc52a98c