An adaptive approach to machine learning for compact particle accelerators

Abstract Machine learning (ML) tools are able to learn relationships between the inputs and outputs of large complex systems directly from data. However, for time-varying systems, the predictive capabilities of ML tools degrade if the systems are no longer accurately represented by the data with whi...

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Autores principales: Alexander Scheinker, Frederick Cropp, Sergio Paiagua, Daniele Filippetto
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
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Acceso en línea:https://doaj.org/article/1b5bd259315f456690de4a256e722122
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