Logo image
Back
Applications of Deep Learning to physics workflows
Preprint   Open access

Applications of Deep Learning to physics workflows

Manan Agarwal, Jay Alameda, Jeroen Audenaert, Will Benoit, Damon Beveridge, Meghna Bhattacharya, Chayan Chatterjee, Deep Chatterjee, Andy Chen, Muhammed Saleem Cholayil, …
arXiv.org
13 Jun 2023
DOI: https://doi.org/10.48550/arxiv.2306.08106
url
https://doi.org/10.48550/arXiv.2306.08106View
Preprint (Author's original) Open arXiv.org - Non-exclusive license to distribute

Abstract

Physics - General Relativity and Quantum Cosmology Physics - High Energy Astrophysical Phenomena Physics - High Energy Physics - Experiment High Energy Astrophysics Relativity
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.

Metrics

18 Record Views

Details

Logo image