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Physics-informed neural network method for modelling beam-wall interactions

arXiv 2112.11323 ↗First posted 21 Dec 2021physics.acc-ph cs.LG cs.NAcited by 0

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Abstract (arXiv)

A mesh-free approach for modelling beam-wall interactions in particle accelerators is proposed. The key idea of our method is to use a deep neural network as a surrogate for the solution to a set of partial differential equations involving the particle beam, and the surface impedance concept. The proposed approach is applied to the coupling impedance of an accelerator vacuum chamber with thin conductive coating, and also verified in comparison with the existing analytical formula.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 14 references that are in the map. First posted on arXiv 21 Dec 2021.

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References · 2

  1. 2017Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equationscited by 373
  2. 2020Physics-Based Deep Neural Networks for Beam Dynamics in Charged Particle Acceleratorscited by 7

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