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Record Nr. |
UNINA9911009336803321 |
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Autore |
Jalas Sören |
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Titolo |
Machine Learning Based Optimization of Laser-Plasma Accelerators / / by Sören Jalas |
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Pubbl/distr/stampa |
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Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 |
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ISBN |
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Edizione |
[1st ed. 2025.] |
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Descrizione fisica |
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1 online resource (210 pages) |
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Collana |
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Springer Theses, Recognizing Outstanding Ph.D. Research, , 2190-5061 |
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Disciplina |
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Soggetti |
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Plasma accelerators |
Machine learning |
Particle accelerators |
Mathematical optimization |
Plasma-based Accelerators |
Machine Learning |
Accelerator Physics |
Optimization |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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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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Sommario/riassunto |
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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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