Abstract
Particle accelerators are enabling tools for scientific exploration and discovery in various disciplines. However, finding optimised operation points for these complex machines is a challenging task due to the large number of parameters involved and the underlying non-linear dynamics. Here, we introduce two families of data-driven surrogate models, based on deep and invertible neural networks, that can replace the expensive physics computer models. These models are employed in multi-objective optimisations to find Pareto optimal operation points for two fundamentally different types of particle accelerators. Our approach reduces the time-to-solution for a multi-objective accelerator optimisation up to a factor of 640 and the computational cost up to 98%. The framework established here should pave the way for future online and real-time multi-objective optimisation of particle accelerators.
Highlights
Flexible Particle AcceleratorAdvances in accelerator science and technology have enabled discoveries in particle physics and other fields—from chemistry and biology to medical applications—for more than a century [1], with no end in sight [2]
We assess the quality of the surrogate models by evaluating the adjusted coefficient of determination ( R2 ) and the relative prediction error at 95% confidence on the test set; i.e., data that have not been used during the development of the models
We have introduced a novel flexible forward surrogate model that is capable of simulating high-fidelity physics models at a plethora of positions along the accelerator
Summary
Flexible Particle AcceleratorAdvances in accelerator science and technology have enabled discoveries in particle physics and other fields—from chemistry and biology to medical applications—for more than a century [1], with no end in sight [2]. Particle accelerators consist of a multitude of building blocks, and the relationship between changes in machine settings, known as design variables, and the corresponding particle-beam response is often non-linear. For this reason, the development and operation of a particle accelerator rely heavily on computational models. The development and operation of a particle accelerator rely heavily on computational models These models use first principles of physics to state the equations of motion, while numerical algorithms are employed to solve them. These models provide valuable insight, but their high computational cost is prohibitive for many applications. A single simulation of the Argonne Wakefield Accelerator (AWA) (Figure 1) takes approximately ten minutes with the high-fidelity physics model Object-Oriented Parallel
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