Speakers
Description
Many accelerator physics problems, such as beamline design, beam dynamics model calibration, online tuning and phase space measurements rely on solving high-dimensional optimisation problems over beam dynamics simulations. Numerical optimisers have successfully been applied to such tasks, but they struggle as the dimensionality and complexity of the objective function increase. In machine learning, gradient-based optimisation algorithms are successfully used to optimise billions of model parameters over complex loss functions when training large neural network models. This is made possible by reverse-mode automatic differentiation, which enables the fast computation of gradients of complex functions. In this tutorial, you will learn to use novel beam dynamics simulations with support for automatic differentiation to your advantage and harness the power of gradient-based optimisation in accelerator physics. Multiple hands-on examples using the Cheetah beam dynamics code will allow you to try these methods for yourself. While we will present multiple example applications of gradient-based optimisation on differentiable beam dynamics simulators, the space of potential applications here is vast, and we believe that participants will go on to discover numerous novel applications for differentiable beam dynamics simulations that were intractable to solve with existing methods.
