Stony Brook University 1-3 credit PHY
Optimization techniques are key to both the design and operation of contemporary charged particle accelerator systems. In addition, machine learning techniques are now being increasingly used, either to augment the capabilities of standard optimization (e.g. through surrogate modeling), or to address entirely new tasks (e.g. anomaly detection, fault classification). This course will introduce a number of optimization and machine learning techniques that are commonly used for particle accelerators, as well as their range of applicability and limitations. 1-3 credits Grading Letter graded (A, A-, B+, etc.)
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