TUA1WC04
CSU, Fort Collins, Colorado, USA
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TUA1WC04 |
Applications of Neural Networks to the Modeling and Control of Particle Accelerators | |
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| Particle accelerators are host to myriad control challenges: they involve a multitude of interacting systems, are often subject to tight performance demands, in many cases exhibit nonlinear behavior, sometimes are not well-characterized due to practical and/or fundamental limitations, and should be able to run for extended periods of time with minimal interruption. One avenue toward improving the way these systems are controlled is to incorporate techniques from machine learning. Within machine learning, neural networks in particular are appealing because they are highly flexible, they are well-suited to problems with nonlinear behavior and large parameter spaces, and their recent success in other fields is an encouraging indicator that they are now technologically mature enough to be fruitfully applied to particle accelerators. This talk will highlight how machine learning in general can be applied to particle accelerator modeling and control by discussing several examples that were focused specifically on neural network-based approaches for several particle accelerator systems and subsystems. | ||
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Slides TUA1WC04 [57.957 MB] | |
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