Machine Learning Based Optimization of Laser-Plasma Accelerators

Erscheinungsjahr 2025
ISBN: 978-3-031-88082-7
Verlag: Springer Nature Switzerland

Buch, Englisch, 134 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 459 g

Reihe: Springer Theses

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.

Jalas, Sören

Fachgebiete

• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Optimierung
• Naturwissenschaften Physik Thermodynamik Plasmaphysik
• Naturwissenschaften Physik Quantenphysik Teilchenphysik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen

Inhaltsverzeichnis

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.