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Jalas Machine Learning Based Optimization of Laser-Plasma Accelerators
1. Auflage 2025
ISBN: 978-3-031-88083-4
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark
E-Book, Englisch, 134 Seiten
Reihe: Springer Theses
ISBN: 978-3-031-88083-4
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark
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.
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
Principles of Laser-Plasma Acceleration.- Bayesian Optimization.- Bayesian Optimization of Plasma Accelerator Simulations.- Experimental Setup: The LUX Laser-Plasma Accelerator.- Bayesian Optimization of a Laser-Plasma Accelerator.- Tuning Curves for a Laser-Plasma Accelerator.- Conclusion.
