JACoW is a publisher in Geneva, Switzerland that publishes the proceedings of accelerator conferences held around the world by an international collaboration of editors.
@unpublished{edelen:ipac2022-moiysp1,
author = {A.L. Edelen},
title = {{Machine Learning as a Tool for Online, Surrogate Modelling of Beam Dynamics}},
booktitle = {Proc. IPAC'22},
language = {english},
intype = {presented at the},
series = {International Particle Accelerator Conference},
number = {13},
venue = {Bangkok, Thailand},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {07},
year = {2022},
note = {presented at IPAC'22 in Bangkok, Thailand, unpublished},
abstract = {{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.}},
}