Machine Learning Based Optimization of Laser-Plasma Accelerators
Series: Springer Theses;
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77 157 Ft
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Product details:
- Publisher Springer
- Date of Publication 25 June 2025
- Number of Volumes 1 pieces, Book
- ISBN 9783031880827
- Binding Hardback
- No. of pages138 pages
- Size 235x155 mm
- Language English
- Illustrations 64 Illustrations, black & white 700
Categories
Short description:
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.
MoreLong description:
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.
MoreTable of Contents:
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.
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