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| URL | https://doi.org/10.18429/JACoW-IPAC2023-WEPA101 |
|---|---|
| Title | Hybrid beamline element ML-training for surrogates in the impactX beam-dynamics code |
| Authors |
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| Abstract | The modeling of current and next-generation particle accelerators is a complex endeavour, ranging from the simulation-guided exploration of advanced lattice elements, over design, to commissioning and operations. This paper explores hybrid beamline modeling, towards coupling s-based particle-in-cell beam dynamics with machine-learning (ML) surrogate models. As a first example, we train a surrogate model of an advanced accelerator element, a laser-wakefield accelerator stage, via the time-based particle-in-cell code WarpX [1]. A second example trains trains a model for the IOTA nonlinear lens via the s-based code ImpactX [2]. |
| Paper | download: WEPA101.pdf |
| Cite | BibTeX, LaTeX, Text/Word, RIS, EndNote |
| Conference | 14th International Particle Accelerator Conference |
| Series | |
| Location | Venice, Italy |
| Date | 07-12 May 2023 |
| Publisher | JACoW Publishing, Geneva, Switzerland |
| Editorial Board | Ralph Assmann - Deutsches Elektronen-Synchrotron DESY Peter McIntosh - Science and Technology Facilities Council (STFC/DL/ASTeC) Giovanni Bisoffi - Istituto Nazionale di Fisica Nucleare (INFN/LNL) Alessandro Fabris - Elettra-Sincrotrone Trieste S.C.p.A. Ivan Andrian - Elettra-Sincrotrone Trieste S.C.p.A. Giulia Vinicola - Istituto Nazionale di Fisica Nucleare (INFN/LNF) |
| Online ISBN | 978-3-95450-231-8 |
| Online ISSN | 2673-5490 |
| Received | 04 May 2023 |
| Revised | 18 May 2023 |
| Accepted | 23 June 2023 |
| Issued | 26 September 2023 |
| DOI | 10.18429/JACoW-IPAC2023-WEPA101 |
| Pages | 2885-2888 |
| 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. |