Machine Learning Based Optimization of Laser-Plasma Accelerators
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...
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| Natura: | Elettronico eBook |
| Lingua: | inglese |
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Cham :
Springer Nature Switzerland : Imprint: Springer,
2025.
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| Edizione: | 1st ed. 2025. |
| Serie: | Springer Theses, Recognizing Outstanding Ph.D. Research,
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| Accesso online: | https://doi.org/10.1007/978-3-031-88083-4 |
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| 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. |
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| Descrizione fisica: | XXXVII, 134 p. 64 illus., 63 illus. in color. online resource. |
| ISBN: | 9783031880834 |
| ISSN: | 2190-5061 |
| DOI: | 10.1007/978-3-031-88083-4 |