Machine Learning Based Optimization of Laser-Plasma Accelerators / / by Sören Jalas

• Autore: Jalas Sören
• Pubblicazione: Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025
• Edizione: 1st ed. 2025.
• Descrizione fisica: 1 online resource (210 pages)
• Disciplina: 530.44
• Soggetto topico: Plasma accelerators; Machine learning; Particle accelerators; Mathematical optimization; Plasma-based Accelerators; Machine Learning; Accelerator Physics; Optimization
• Nota di contenuto: 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.
• Sommario/riassunto: 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.
• Titolo autorizzato: Machine Learning Based Optimization of Laser-Plasma Accelerators
• ISBN: 3-031-88083-8
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9911009336803321
• Lo trovi qui: Univ. Federico II
• Serie: Springer Theses, Recognizing Outstanding Ph.D. Research, . 2190-5061