Speaker
Description
Accelerator design, optimization and simulation, are important steps in the construction of future accelerator-based facilities. Typically, multiple alternatives are investigated, involving multiple design iterations, before selecting a final design. Once a design is selected, the focus shifts to the detailed design of individual accelerator components and end-to-end lattice design and optimization. This phase requires extensive, large-scale and time-consuming electromagnetics and beam dynamics simulations. AI-ML can significantly accelerate and integrate this design process. Additionally, there is significant potential for new discoveries in unexplored areas of the design space, which is currently constrained by the inability to visualize hidden correlations and the limited resources available for exploration. Generative AI can be instrumental in addressing these challenges by uncovering patterns and optimizing designs efficiently. These developments aim to (i) speed up the accelerator design process by developing and applying AI-ML techniques for the design, optimization and simulation of individual accelerator components and full lattices, and (ii) harness the discovery potential of AI-ML by deploying existing Generative AI tools for accelerator design, evaluating their strengths and weaknesses, developing and implementing improvements to enhance their effectiveness for these design tasks.
