Journals of Accelerator Conferences Website (JACoW)

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URLhttps://doi.org/10.18429/JACoW-IPAC2026-THP5325
TitleSciBmad: A differentiable, GPU-parallelized software library for particle accelerator design, nonlinear analysis, and machine learning
Authors
  • M. Signorelli, D. Sagan, J. Devlin
    Cornell University (CLASSE)
  • G. Hoffstaetter
    Cornell University
AbstractWe present SciBmad, a new accelerator physics software library developed to meet the needs of all accelerator design, analysis, and virtual modeling. SciBmad consists of a set of modular packages featuring fully differentiable and CPU/GPU-parallelized symplectic integrators, spin tracking and radiation, flexible and differentiable lattice definitions, nonlinear normal form analysis tools with Lie algebraic methods, batch parameter parallelization, and more. It is fully usable in Julia and easily callable from Python, making it easy to integrate it with external optimizers and machine learning frameworks. All releases of SciBmad undergo rigorous, automated testing with maximal code coverage, and all integrators are validated against PTC. With a growing list of features and contributors, SciBmad aims to be a powerful tool for any accelerator physics application.
Paperdownload: THP5325.pdf
CiteBibTeX, LaTeX, Text/Word, RIS, EndNote
Conference17th International Particle Accelerator Conference
Series
LocationDeauville, France
Date17-22 May 2026
PublisherJACoW Publishing, Geneva, Switzerland
Editorial BoardEditorial Board
Online ISBN978-3-95450-252-3
Online ISSN2673-5350
Received13 May 2026
Revised18 May 2026
Accepted
Issued20 July 2026
DOI10.18429/JACoW-IPAC2026-THP5325
Pages4737-4740