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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},
}