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Applications of Deep Learning to physics workflows

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Is a
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Academic paper
0

Academic Paper attributes

arXiv ID
2306.081060
arXiv Classification
Physics
Physics
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Publication URL
arxiv.org/pdf/2306.0...06.pdf0
Publisher
ArXiv
ArXiv
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DOI
doi.org/10.48550/ar...06.081060
Paid/Free
Free0
Academic Discipline
Physics
Physics
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Quantum cosmology
Quantum cosmology
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Astrophysics
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Particle physics
Particle physics
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General relativity
General relativity
0
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High-energy astronomy
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Submission Date
June 13, 2023
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Author Names
Pooyan Goodarzi0
Weichangfeng Guo0
Will Benoit0
William Patrick McCormack0
Xiangyang Ju0
Xiwei Wang0
Yongbin Feng0
Alec Gunny0
...
Paper abstract

Modern large-scale physics experiments create datasets with sizes and streaming rates that can exceed those from industry leaders such as Google Cloud and Netflix. Fully processing these datasets requires both sufficient compute power and efficient workflows. Recent advances in Machine Learning (ML) and Artificial Intelligence (AI) can either improve or replace existing domain-specific algorithms to increase workflow efficiency. Not only can these algorithms improve the physics performance of current algorithms, but they can often be executed more quickly, especially when run on coprocessors such as GPUs or FPGAs. In the winter of 2023, MIT hosted the Accelerating Physics with ML at MIT workshop, which brought together researchers from gravitational-wave physics, multi-messenger astrophysics, and particle physics to discuss and share current efforts to integrate ML tools into their workflows. The following white paper highlights examples of algorithms and computing frameworks discussed during this workshop and summarizes the expected computing needs for the immediate future of the involved fields.

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