Journals of Accelerator Conferences Website (JACoW)
JACoW
is a publisher in Geneva, Switzerland that publishes the
proceedings of accelerator conferences held around the world
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| URL | https://doi.org/10.18429/JACoW-IPAC2026-THP5364 |
|---|---|
| Title | Accelerator optimizations using SciBmad with PyTorch |
| Authors |
|
| Abstract | SciBmad is a new, fully differentiable software ecosystem for accelerator physics, usable in Julia and/or Python. Full differentiability enables efficient and numerically-reliable optimizations of particle accelerators, in particular with machine learning (ML) methods. One of the most commonly used ML frameworks is PyTorch, which provides powerful, versatile, and straightforward tools for such purposes. We implemented PyTorch bindings with SciBmad, enabling seamless integration of SciBmad’s powerful and differentiable simulations with a standard PyTorch workflow. With these bindings, all of PyTorch’s powerful tools can be used easily with SciBmad. In this paper, we describe the implementation details and present some examples demonstrating SciBmad-PyTorch workflows. |
| Paper | download: THP5364.pdf |
| Cite | BibTeX, LaTeX, Text/Word, RIS, EndNote |
| Conference | 17th International Particle Accelerator Conference |
| Series | |
| Location | Deauville, France |
| Date | 17-22 May 2026 |
| Publisher | JACoW Publishing, Geneva, Switzerland |
| Editorial Board | Editorial Board |
| Online ISBN | 978-3-95450-252-3 |
| Online ISSN | 2673-5350 |
| Received | 13 May 2026 |
| Revised | 06 June 2026 |
| Accepted | |
| Issued | 20 July 2026 |
| DOI | 10.18429/JACoW-IPAC2026-THP5364 |
| Pages | 4810-4813 |
| 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. |