Speakers
Description
A major challenge in constructing future nuclear fusion power plants is understanding how reactor materials are damaged by the neutron flux generated during the fusion process. In order to address this challenge, the IFMIF-DONES neutron source is being built for material irradiation, generating the necessary neutron flux through a stripping reaction between accelerated deuterons and a lithium circuit. In this work, within the DONES-FLUX project, Fourier Neural Operators are employed as Deep Learning Surrogate Models for optimizing the design of the High-Energy Beam Transport Line of the IFMIF-DONES accelerator. The trained models, which predict the deuteron beam statistical functions and beam profile distributions, are roughly 3 orders of magnitude faster than traditional simulations while keeping mean absolute percentage errors below 5%. This significant reduction in inference time, along with the models' differentiability, enables the use of optimization algorithms like online Reinforcement Learning, Bayesian Optimization, and Gradient Descent. Additionally, this last method was implemented and tested for finding optimal quadrupole values for different beam configurations, where solutions are reached within minutes. These positive results highlight the synergy between different Deep Learning architectures and offer a promising collaboration between the field of Artificial Intelligence and accelerator facilities.
