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@INPROCEEDINGS{Huebl:636304,
author = {Huebl, Axel and Mitchell, Chad and Lehe, Remi and Formenti,
A. and Charleux, Grégoire and Myers, Andrew and Zhang,
Weiqun and Qiang, Ji and Vay, Jean-Luc and Kaiser, Jan and
Hespe, Christian and Gonzalez-Aguilera, Juan Pablo and Xu,
Chenran and Santamaria Garcia, Andrea and Roussel, Ryan and
Edelen, Auralee and Moses, William Steven},
title = {{T}owards {D}ifferentiable {B}eam {D}ynamics {M}odeling in
{BLAST}/{I}mpact{X}},
journal = {JACoW NAPAC2025 (2026) TUP101},
address = {Geneva},
publisher = {JACoW Publishing},
reportid = {PUBDB-2025-03643},
isbn = {978-3-95450-261-5},
pages = {614 - 617},
year = {2025},
note = {Literaturangaben;},
comment = {[E-Book] NAPAC 2025 : North American Particle Accelerator
Conference, August 10-15, 2025, SAFE Credit Union,
Convention Center, Sacramento, California / Wang, Ling ,
[Geneva, Switzerland] : JACoW Publishing, [2025],},
booktitle = {[E-Book] NAPAC 2025 : North American
Particle Accelerator Conference, August
10-15, 2025, SAFE Credit Union,
Convention Center, Sacramento,
California / Wang, Ling , [Geneva,
Switzerland] : JACoW Publishing,
[2025],},
abstract = {Differentiable simulations are in demand in accelerator
physics, demonstrating order-of-magnitude improvements for
complex tasks such as many-parameter optimization for
accelerator working points and reconstruction of
hard-to-measure quantities. At its core, a differentiable
simulation does not only solve a forward problem, but
additionally provides gradients of output parameters (e.g.
beam parameters) with respect to input parameters (e.g.
beamline or source parameters).How to effectively program
large dynamic simulations differentiably is still an open
question, but there is general consensus that a
“single-source” approach aided by automatic
differentiation (AD) is desirable. Addressing this, there
are a) emerging domain-specific languages in machine
learning that are intrinsically differentiable, and b)
highly-performing $\&$ scalable, general-purpose languages
like ISO C++ of existing codes. The challenge of approach a)
is syntax specialization, which can limit ease of
implementation $\&$ performance for physics algorithms,
while b) requires additional work for AD.Performance is
important for modeling high-order beam dynamics and
collective effects in accelerators. We compare the fast,
modern codes ImpactX (C++/Python) and Cheetah (PyTorch)
using traditional, gradient-free modeling. We then show
progress in introducing single-source differentiability in
ImpactX using modern compiler techniques, producing
performant executables for gradient-based and gradient-free
modeling.},
month = {Aug},
date = {2025-08-10},
organization = {North American Particle Accelerator
Conference , Sacramento, CA (USA), 10
Aug 2025 - 15 Aug 2025},
keywords = {Accelerator Physics (Other) /
mc5-beam-dynamics-and-em-fields - MC5 – Beam Dynamics and
EM Fields (Other) / simulation (autogen) / GPU (autogen) /
acceleration (autogen) / laser (autogen) / electron
(autogen) / electronics (autogen)},
cin = {MSK},
cid = {I:(DE-H253)MSK-20120731},
pnm = {621 - Accelerator Research and Development (POF4-621)},
pid = {G:(DE-HGF)POF4-621},
experiment = {EXP:(DE-H253)ARES-20200101},
typ = {PUB:(DE-HGF)16 / PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
doi = {10.18429/JACoW-NAPAC2025-TUP101},
url = {https://bib-pubdb1.desy.de/record/636304},
}