BÜCHER VERSANDKOSTENFREI INNERHALB DEUTSCHLANDS
Machine Learning Based Optimization of Laser-Plasma Accelerators
Springer
ISBN 978-3-031-88082-7
Standardpreis
Bibliografische Daten
Fachbuch
Buch. Hardcover
2025
1 s/w-Abbildung, 63 Farbabbildungen.
In englischer Sprache
Umfang: xxxvii, 134 S.
Format (B x L): 15,5 x 23,5 cm
Verlag: Springer
ISBN: 978-3-031-88082-7
Weiterführende bibliografische Daten
Das Werk ist Teil der Reihe: Springer Theses
Produktbeschreibung
This book explores the application of machine learning-based methods, particularly Bayesian optimization, within the realm of laser-plasma accelerators. The book involves the implementation of Bayesian optimization to fine tune the parameters of the lux accelerator, encompassing simulations and real-time experimentation.
In combination, the methods presented in this book provide valuable tools for effectively managing the inherent complexity of LPAs, spanning from the design phase in simulations to real-time operation, potentially paving the way for LPAs to cater to a wide array of applications with diverse demands.
In combination, the methods presented in this book provide valuable tools for effectively managing the inherent complexity of LPAs, spanning from the design phase in simulations to real-time operation, potentially paving the way for LPAs to cater to a wide array of applications with diverse demands.
Autorinnen und Autoren
Kundeninformationen
Nominated as an outstanding thesis by Hamburg University and Desy Features machine learning-based methods to optimize laser-plasma accelerators Identifies the methods for autonomous tuning on generating high quality electron beams
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