Texas A&M University Public Partnership & Outreach
Optimization and Machine Learning for Accelerators
This class is full and the waiting list is full
Purpose and Audience
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. The course is suitable for graduate students in physics or engineering as well as postdocs, and staff members at laboratories or companies.
Familiarity with accelerator science and technology at the level of USPAS Fundamentals of Accelerator Physics and Technology with Simulations and Measurements Lab or USPAS graduate-level Accelerator Physics is strongly recommended. For example, prospective students should be familiar with standard accelerator system terms such as ‘quadrupole magnet’,’ rf cavity’, ‘emittance’, ‘bunch length’, etc.
Experience with Python 3 basics, common Python packages (numpy, h5py, matplotlib, scipy), and github are required. All homework and laboratory assignments will use Python 3 based iPython notebooks. Python basics will NOT be covered during the course. There will NOT be a Python tutorial on the first day. Students can both assess their experience and prepare if they have any specific needs by viewing the short tutorials and background reading below. It is essential that enrolled students meet these requirements. There will not be time during the course to catch up if unfamiliar.
Scipy lecture notes: https://scipy-lectures.org/
Students must be competent with materials at the level presented and must carry out independent study in advance to rectify any needs in cases of deficiencies.
It is the responsibility of the student to ensure that they meet the course prerequisites or have equivalent experience.
The course will cover the basics of various optimization and machine learning techniques that are presently being used or are under consideration for applications in the context of particle accelerators. The aim is to give the students an overview of these techniques, and of the specific accelerator-related task that they can solve. In particular, for a given task (e.g. optimization, classification), the relative benefits and limitations of different techniques will be emphasized (e.g. sensitivity to hyperparameters, robustness to local minima, etc.). A large fraction of the course will be dedicated to hands-on programming sessions. All the sessions will use open-source, widely-supported Python packages for optimization and machine learning. Packages to be employed include: numpy, h5py, matplotlib, keras, and tensorflow. At the end of the course, attendants should be able to directly apply these tools to their respective topics of research.
The morning and afternoon sessions will be split into lecture and computer laboratory time. Short lectures on the lab subjects will be given, then students will work on the laboratory assignment during class time with instructors present. Students will use their laptops to complete the laboratory assignments. Homework will be regularly assigned, and instructors and TAs will be available to help answer questions during evening homework sessions.
The course topics include:
Machine Learning (ML) Techniques
All materials needed for the course will be provided by the instructors via a course web site.
The course is expected to be similar to a prior version given in a Summer 2021 session: https://slaclab.github.io/USPAS_ML/ . Those wishing to prepare can study materials from this previous version.
This course will be taught using iPython notebooks, with computing provided for free by RadiaSoft. Students must have a laptop or tablet computer with an html5 compatible browser.