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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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Autor principal: Jalas, Sören (Autor)
Autor corporatiu: SpringerLink (Online service)
Format: Electrònic eBook
Idioma:anglès
Publicat: Cham : Springer Nature Switzerland : Imprint: Springer, 2025.
Edició:1st ed. 2025.
Col·lecció:Springer Theses, Recognizing Outstanding Ph.D. Research,
Matèries:
Plasma accelerators.
Machine learning.
Particle accelerators.
Mathematical optimization.
Plasma-based Accelerators.
Machine Learning.
Accelerator Physics.
Optimization.
Accés en línia:https://doi.org/10.1007/978-3-031-88083-4
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Taula de continguts:
  • 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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