THPB068

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URL	https://doi.org/10.18429/JACoW-LINAC2024-THPB068
Title	Advancements in backwards differentiable beam dynamics simulations for accelerator design, model calibration, and machine learning
Authors	R. Roussel, A. Edelen SLAC National Accelerator Laboratory J. Gonzalez-Aguilera University of Chicago R. Lehe, A. Huebl, G. Charleux Lawrence Berkeley National Laboratory J. Kaiser, A. Eichler Deutsches Elektronen-Synchrotron, Deutsches Elektronen-Synchrotron DESY A. Santamaria Garcia, C. Xu Karlsruhe Institute of Technology
Abstract	Many accelerator physics problems such as beamline design, beam dynamics model calibration or interpreting experimental measurements rely on solving an optimization problem that use a simulation of beam dynamics. However, it is difficult to solve high dimensional optimization problems using current beam dynamics simulations because calculating gradients of simulated objectives with respect to input parameters is computationally expensive in high dimensions. To address this problem, backwards differentiable beam dynamics simulations have been developed that enable computationally inexpensive calculations of objective gradients that are independent of the number of input parameters. In this work, we highlight current and future applications of differentiable beam dynamics simulations in accelerator physics, such as improving accelerator design, model calibration, and machine learning. We also describe current collaborative efforts between SLAC, DESY, KIT, and LBNL to implement fast, backwards differentiable beam dynamics simulations in Python. These tools will enable unprecedented improvements in optimization efficiency and speed when using beam dynamics simulations, leading to enhanced control and detailed understanding of physical accelerator systems.
Paper	download: THPB068.pdf
Cite	BibTeX, LaTeX, Text/Word, RIS, EndNote
Conference	32nd Linear Accelerator Conference (LINAC2024)
Series
Location	Chicago, IL, USA
Date	25-30 Aug 2024
Publisher	JACoW Publishing, Geneva, Switzerland
Editorial Board	Kelly Jaje (ANL), Kip Bishofberger (LANL), Ryan Brody (ANL), Gustavo Bruno (ANL), Jan Chrin (PSI), Cathy Eyberger (ANL), Maggie Montes (SLAC), Denise Skiadopoulos (ANL), Ling Wang (MSU/FRIB), Kent Wootton (ANL)
Online ISBN	978-3-95450-219-6
Online ISSN	2226-0366
Received	20 August 2024
Revised	29 August 2024
Accepted	29 August 2024
Issued	21 August 2025
DOI	10.18429/JACoW-LINAC2024-THPB068
Pages	768-771
Copyright	Published by JACoW Publishing under the terms of the Creative Commons Attribution 4.0 license. Any further distribution of this work must maintain attribution to the author(s), the published article's title, publisher, and DOI.