Turn-key constrained parameter space exploration for particle accelerators using Bayesian active learning

Characterizing an unknown, complex system, like an accelerator, in multi-dimensional space is a challenging task. Here the authors report a Bayesian active learning method - Constrained Proximal Bayesian Exploration - for the characterization of a complex, constrained measurement as a function of mu...

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Autores principales:	Ryan Roussel, Juan Pablo Gonzalez-Aguilera, Young-Kee Kim, Eric Wisniewski, Wanming Liu, Philippe Piot, John Power, Adi Hanuka, Auralee Edelen
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/07110b4ee93f4bd4919f427ea5497766
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spelling	oai:doaj.org-article:07110b4ee93f4bd4919f427ea54977662021-12-02T15:14:56ZTurn-key constrained parameter space exploration for particle accelerators using Bayesian active learning10.1038/s41467-021-25757-32041-1723https://doaj.org/article/07110b4ee93f4bd4919f427ea54977662021-09-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-25757-3https://doaj.org/toc/2041-1723Characterizing an unknown, complex system, like an accelerator, in multi-dimensional space is a challenging task. Here the authors report a Bayesian active learning method - Constrained Proximal Bayesian Exploration - for the characterization of a complex, constrained measurement as a function of multiple free parameters.Ryan RousselJuan Pablo Gonzalez-AguileraYoung-Kee KimEric WisniewskiWanming LiuPhilippe PiotJohn PowerAdi HanukaAuralee EdelenNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-7 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Ryan Roussel Juan Pablo Gonzalez-Aguilera Young-Kee Kim Eric Wisniewski Wanming Liu Philippe Piot John Power Adi Hanuka Auralee Edelen Turn-key constrained parameter space exploration for particle accelerators using Bayesian active learning
description	Characterizing an unknown, complex system, like an accelerator, in multi-dimensional space is a challenging task. Here the authors report a Bayesian active learning method - Constrained Proximal Bayesian Exploration - for the characterization of a complex, constrained measurement as a function of multiple free parameters.
format	article
author	Ryan Roussel Juan Pablo Gonzalez-Aguilera Young-Kee Kim Eric Wisniewski Wanming Liu Philippe Piot John Power Adi Hanuka Auralee Edelen
author_facet	Ryan Roussel Juan Pablo Gonzalez-Aguilera Young-Kee Kim Eric Wisniewski Wanming Liu Philippe Piot John Power Adi Hanuka Auralee Edelen
author_sort	Ryan Roussel
title	Turn-key constrained parameter space exploration for particle accelerators using Bayesian active learning
title_short	Turn-key constrained parameter space exploration for particle accelerators using Bayesian active learning
title_full	Turn-key constrained parameter space exploration for particle accelerators using Bayesian active learning
title_fullStr	Turn-key constrained parameter space exploration for particle accelerators using Bayesian active learning
title_full_unstemmed	Turn-key constrained parameter space exploration for particle accelerators using Bayesian active learning
title_sort	turn-key constrained parameter space exploration for particle accelerators using bayesian active learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/07110b4ee93f4bd4919f427ea5497766
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Turn-key constrained parameter space exploration for particle accelerators using Bayesian active learning

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