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Machine learning based optimization of laser-plasma accelerators

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning based optimization of laser-plasma accelerators/ by Sören Jalas.
Author:	Jalas, Sören.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	xxxvii, 134 p. :ill. (chiefly color), digital ;24 cm.
Notes:	"Doctoral thesis accepted by Universität Hamburg, Hamburg, Germany."
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Plasma accelerators. -
Online resource:	https://doi.org/10.1007/978-3-031-88083-4
ISBN:	9783031880834

Machine learning based optimization of laser-plasma accelerators
Jalas, Sören.

Machine learning based optimization of laser-plasma accelerators[electronic resource] /by Sören Jalas. - Cham :Springer Nature Switzerland :2025. - xxxvii, 134 p. :ill. (chiefly color), digital ;24 cm. - Springer theses,2190-5061. - Springer theses..

"Doctoral thesis accepted by Universität Hamburg, Hamburg, Germany."

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.

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.

ISBN: 9783031880834

Standard No.: 10.1007/978-3-031-88083-4doiSubjects--Topical Terms:

2132619
Plasma accelerators.

LC Class. No.: QC718.5.M36

Dewey Class. No.: 538.6

Machine learning based optimization of laser-plasma accelerators
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520 $a 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.
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