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- Title
Surrogate model of particle accelerators using encoder–decoder neural networks with physical regularization.
- Authors
Sun, Kunxiang; Chen, Xiaolong; Zhao, Xiaoying; Qi, Xin; Wang, Zhijun; He, Yuan
- Abstract
Accelerator engineering could benefit from faster and higher-quality physics simulations. Machine learning has emerged as a promising tool for developing accelerator simulation programs that are both fast and accurate. In this study, we propose a surrogate model based on encoder–decoder neural networks. We incorporate physical regularization into the loss function, which allows us to integrate prior physical knowledge into the deep learning network. The advantage of this regularization is that it ensures the results are more consistent with the underlying physical laws. The method was applied to beam dynamics modeling of the medium energy beam transport (MEBT) section in the China Accelerator Facility for Superheavy Elements (CAFe II). The final results indicate that after training, the network maintains a mismatch, emittance difference, and transmission efficiency of approximately 0.01. Our scheme has been demonstrated to be effective in the simulation of the accelerator's beam dynamics.
- Subjects
BEAM dynamics; SUPERHEAVY elements; PHYSICAL laws; DEEP learning; MACHINE learning; SIMULATION software; PARTICLE accelerators
- Publication
International Journal of Modern Physics A: Particles & Fields; Gravitation; Cosmology; Nuclear Physics, 2023, Vol 38, Issue 26/27, p1
- ISSN
0217-751X
- Publication type
Article
- DOI
10.1142/S0217751X23501452