JACoW is a publisher in Geneva, Switzerland that publishes the proceedings of accelerator conferences held around the world by an international collaboration of editors.
TY - UNPB AU - Edelen, A.L. ED - Zimmermann, Frank ED - Tanaka, Hitoshi ED - Sudmuang, Porntip ED - Klysubun, Prapong ED - Sunwong, Prapaiwan ED - Chanwattana, Thakonwat ED - Petit-Jean-Genaz, Christine ED - Schaa, Volker R.W. TI - Machine Learning as a Tool for Online, Surrogate Modelling of Beam Dynamics J2 - Proc. of IPAC2022, Bangkok, Thailand, 12-17 June 2022 CY - Bangkok, Thailand T2 - International Particle Accelerator Conference T3 - 13 LA - english AB - The detailed design and optimization of accelerators has historically relied on high-fidelity simulations whose computational requirements limit their use as online tools. Recently, a growing community has begun reducing this computational burden by applying techniques from machine learning. For example, by learning from a sparse sampling of physics simulations one can develop fast-executing "surrogate models" that approximately predict accelerator performance for entirely new design parameters. Using these models can reduce compute times for multi-objective optimization studies by several orders of magnitude. In addition, surrogate models are now being applied in operational settings to enable non-invasive diagnostics and real-time optimization. This talk will cover developments in this field, applications to medium-energy electron photoinjectors, and how such surrogate models may improve our physics understanding of present and future accelerators. PB - JACoW Publishing CP - Geneva, Switzerland ER -