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-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.

Machine Learning Based Optimization of Laser-Plasma Accelerators features in the following genres: Plasma physics, Machine learning, Optimization, Particle and high-energy physics

Machine Learning Based Optimization of Laser-Plasma Accelerators is available in Hardback

Machine Learning Based Optimization of Laser-Plasma Accelerators was written by Sören Jalas and published by Springer an imprint of Springer Nature Switzerland

Machine Learning Based Optimization of Laser-Plasma Accelerators has 134 pages

Yes it is part of Springer Theses series

£134.99