Applications of Deep Learning to physics workflows
Applications of Deep Learning to physics workflows
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.
Patrick Sutton、Kate Scholberg、Chayan Chatterjee、William Patrick McCormack、Fatima Zahra Lahbabi、Steven Farrell、Batool Safarzadeh Samani、Geoffrey Mo、Manan Agarwal、Frank Wuerthwein、Eric Anton Moreno、Ethan Marx、Janina Hakenmueller、Vasileios Skliris、Michael Norman、Ekaterina Govorkova、Meghna Bhattacharya、Deep Chatterjee、Tingjun Yang、Victoria Ashley Villar、Jared Robbins、Erik Katsavounidis、Shivam Raj、Andrew Naylor、Michael Coughlin、Shih-Chieh Hsu、Shu-Wei Yeh、Alistair McLeod、Javier Mauricio Duarte、Gautham Narayan、Maximilian Dax、Ben Hawks、Joshua Peterson、Jeroen Audenaert、Aman Desai、Manolis Kellis、Kevin Pedro、Alec Gunny、Damon Beveridge、Pratik Jawahar、Xiangyang Ju、Xiwei Wang、Andrea Di Luca、Ryan Raikman、Sunil Choudhary、Niharika Sravan、Muhammed Saleem Cholayil、George Ricker、Alex Schuy、Elham E Khoda、Will Benoit、Linqing Wen、Mark Neubauer、Daniel Muthukrishna、Michael P¨1rrer、Jonathan Guiang、Yongbin Feng、Chia-Jui Chou、Rafia Omer、Pooyan Goodarzi、Matthew Graham、Weichangfeng Guo、Siddharth Soni、Jay Alameda、Van Tha Bik Lian、Mia Liu、Konstantin Malanchev、Andy Chen
物理学计算技术、计算机技术
Patrick Sutton,Kate Scholberg,Chayan Chatterjee,William Patrick McCormack,Fatima Zahra Lahbabi,Steven Farrell,Batool Safarzadeh Samani,Geoffrey Mo,Manan Agarwal,Frank Wuerthwein,Eric Anton Moreno,Ethan Marx,Janina Hakenmueller,Vasileios Skliris,Michael Norman,Ekaterina Govorkova,Meghna Bhattacharya,Deep Chatterjee,Tingjun Yang,Victoria Ashley Villar,Jared Robbins,Erik Katsavounidis,Shivam Raj,Andrew Naylor,Michael Coughlin,Shih-Chieh Hsu,Shu-Wei Yeh,Alistair McLeod,Javier Mauricio Duarte,Gautham Narayan,Maximilian Dax,Ben Hawks,Joshua Peterson,Jeroen Audenaert,Aman Desai,Manolis Kellis,Kevin Pedro,Alec Gunny,Damon Beveridge,Pratik Jawahar,Xiangyang Ju,Xiwei Wang,Andrea Di Luca,Ryan Raikman,Sunil Choudhary,Niharika Sravan,Muhammed Saleem Cholayil,George Ricker,Alex Schuy,Elham E Khoda,Will Benoit,Linqing Wen,Mark Neubauer,Daniel Muthukrishna,Michael P¨1rrer,Jonathan Guiang,Yongbin Feng,Chia-Jui Chou,Rafia Omer,Pooyan Goodarzi,Matthew Graham,Weichangfeng Guo,Siddharth Soni,Jay Alameda,Van Tha Bik Lian,Mia Liu,Konstantin Malanchev,Andy Chen.Applications of Deep Learning to physics workflows[EB/OL].(2023-06-13)[2025-06-15].https://arxiv.org/abs/2306.08106.点此复制
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