{
"item_title" : "Machine Learning Based Optimization of Laser-Plasma Accelerators",
"item_author" : [" Sören Jalas "],
"item_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.
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Overview
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.
This item is Non-Returnable
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Details
- ISBN-13: 9783031880858
- ISBN-10: 3031880854
- Publisher: Springer
- Publish Date: June 2026
- Page Count: 134
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