arXiv · 2306.08106
Applications of Deep Learning to physics workflows
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.
Explore related subjects
Keep this discovery
Explore connections, maps & timelines
Manan Agarwal, Jay Alameda, Jeroen Audenaert, Will Benoit, Damon Beveridge, Meghna Bhattacharya, Chayan Chatterjee, Deep Chatterjee, Andy Chen, Muhammed Saleem Cholayil, Chia-Jui Chou, Sunil Choudhary, Michael Coughlin, Maximilian Dax, Aman Desai, Andrea Di Luca, Javier Mauricio Duarte, Steven Farrell, Yongbin Feng, Pooyan Goodarzi, Ekaterina Govorkova, Matthew Graham, Jonathan Guiang, Alec Gunny, Weichangfeng Guo, Janina Hakenmueller, Ben Hawks, Shih-Chieh Hsu, Pratik Jawahar, Xiangyang Ju, Erik Katsavounidis, Manolis Kellis, Elham E Khoda, Fatima Zahra Lahbabi, Van Tha Bik Lian, Mia Liu, Konstantin Malanchev, Ethan Marx, William Patrick McCormack, Alistair McLeod, Geoffrey Mo, Eric Anton Moreno, Daniel Muthukrishna, Gautham Narayan, Andrew Naylor, Mark Neubauer, Michael Norman, Rafia Omer, Kevin Pedro, Joshua Peterson, Michael Pürrer, Ryan Raikman, Shivam Raj, George Ricker, Jared Robbins, Batool Safarzadeh Samani, Kate Scholberg, Alex Schuy, Vasileios Skliris, Siddharth Soni, Niharika Sravan, Patrick Sutton, Victoria Ashley Villar, Xiwei Wang, Linqing Wen, Frank Wuerthwein, Tingjun Yang, Shu-Wei Yeh. 2023-06-13. Applications of Deep Learning to physics workflows. https://arxiv.org/abs/2306.08106
Cite the original work for its findings. Save a collection to share your selection of sources.