The Joint Accelerator Conferences Website (JACoW) is an international collaboration that publishes the proceedings of accelerator conferences held around the world.
@inproceedings{dalena:ipac2021-thpab201,
author = {B. Dalena and M. Ben Ghali},
title = {{A Machine Learning Technique for Dynamic Aperture Computation}},
booktitle = {Proc. IPAC'21},
pages = {4172--4175},
eid = {THPAB201},
language = {english},
keywords = {network, dynamic-aperture, simulation, hadron, distributed},
venue = {Campinas, SP, Brazil},
series = {International Particle Accelerator Conference},
number = {12},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {08},
year = {2021},
issn = {2673-5490},
isbn = {978-3-95450-214-1},
doi = {10.18429/JACoW-IPAC2021-THPAB201},
url = {https://jacow.org/ipac2021/papers/thpab201.pdf},
note = {https://doi.org/10.18429/JACoW-IPAC2021-THPAB201},
abstract = {{Currently, dynamic aperture calculations of high-energy hadron colliders are performed through computer simulations, which are both a resource-heavy and time-costly processes. The aim of this study is to use a reservoir computing machine learning model in order to achieve a faster extrapolation of dynamic aperture values. A recurrent echo-state network (ESN) architecture is used as a basis for this work. Recurrent networks are better fitted to extrapolation tasks while the reservoir echo-state structure is computationally effective. Model training and validation is conducted on a set of "seeds" corresponding to the simulation results of different machine configurations. Adjustments in the model architecture, manual metric and data selection, hyper-parameters tuning and the introduction of new parameters enabled the model to reliably achieve good performance on examining testing sets.}},
}