IPAC2022 - List of Authors (Stein, O.)

Paper	Title	Page
WEPOMS036	Accelerating Linear Beam Dynamics Simulations for Machine Learning Applications	2330
	O. Stein, I.V. Agapov, A. Eichler, J. Kaiser DESY, Hamburg, Germany
	Machine learning has proven to be a powerful tool with many applications in the field of accelerator physics. Training machine learning models is a highly iterative process that requires large numbers of samples. However, beam time is often limited and many of the available simulation frameworks are not optimized for fast computation. As a result, training complex models can be infeasible. In this contribution, we introduce Cheetah, a linear beam dynamics framework optimized for fast computations. We show that Cheetah outperforms existing simulation codes in terms of speed and furthermore demonstrate the application of Cheetah to a reinforcement-learning problem as well as the successful transfer of the Cheetah-trained model to the real world. We anticipate that Cheetah will allow for faster development of more capable machine learning solutions in the field, one day enabling the development of autonomous accelerators.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-WEPOMS036
About •	Received ※ 07 June 2022 — Revised ※ 14 June 2022 — Accepted ※ 01 July 2022 — Issue date ※ 01 July 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Accelerating Linear Beam Dynamics Simulations for Machine Learning Applications

2330

O. Stein, I.V. Agapov, A. Eichler, J. Kaiser
DESY, Hamburg, Germany

Machine learning has proven to be a powerful tool with many applications in the field of accelerator physics. Training machine learning models is a highly iterative process that requires large numbers of samples. However, beam time is often limited and many of the available simulation frameworks are not optimized for fast computation. As a result, training complex models can be infeasible. In this contribution, we introduce Cheetah, a linear beam dynamics framework optimized for fast computations. We show that Cheetah outperforms existing simulation codes in terms of speed and furthermore demonstrate the application of Cheetah to a reinforcement-learning problem as well as the successful transfer of the Cheetah-trained model to the real world. We anticipate that Cheetah will allow for faster development of more capable machine learning solutions in the field, one day enabling the development of autonomous accelerators.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-WEPOMS036

About •

Received ※ 07 June 2022 — Revised ※ 14 June 2022 — Accepted ※ 01 July 2022 — Issue date ※ 01 July 2022

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)