Speaker
Description
Cyclotron design involves extensive computations for both individual accelerator systems and particle dynamics, requiring careful optimization of numerous interdependent parameters. The integration of artificial intelligence (AI) and machine learning (ML) into cyclotron design and operation is already demonstrating significant benefits, accelerating development, enhancing control, and improving system reliability. AI-driven approaches have the potential to revolutionize cyclotron optimization, enabling researchers to streamline design processes, explore a broader parameter space, and ultimately enhance device performance. This presentation discusses initial results from applying ML to magnetic field shaping and analyzes the broader opportunities and challenges in leveraging AI/ML for virtual cyclotron prototyping. The use of AI in the development of software for beam dynamics, particularly for seamless integration between 3D models, electromagnetic field simulation programs, and particle tracking, has significantly simplified the process and taken it to a new level.
