Abstract
Errors from discretization and large data volume of field maps is a concern for beam dynamics simulations with respect to achievable accuracy and to the required amount of time. High-order singular value decomposition (HOSVD) has recently emerged as simple, effective, and adaptive tool to extract the essentials from multidimensional data. This paper is on the feasibility of compression and noise reduction of electromagnetic field map data with HOSVD. The method has been applied to an electric field map of a DTL cavity with 11 m in length comprising 55 rf-gaps. The original field map data of 220 MB was converted into practically noise-free data of just 20 KB. Noise was reduced by 95% as demonstrated using a cubic cavity for which the analytical field map is available.
Highlights
The application of finite element methods has increased rapidly
In the finite element method (FEM), a major source of error is introduced by the spatial discretization of the problem domain into elements, which were studied vastly [5,6], this part of error is defined as noise
We report on successful application of High-order singular value decomposition (HOSVD) to field map data in order to compress and to de-noise the data provided by solvers
Summary
HOSVD is a multilinear generalization of matrix SVD to high-order tensors, and it can provide for adequate preservation of the data’s essentials through representation of a tensor using appropriate bases [7]. This decomposition plays an important role in various domains as spectral analysis [8], communication and radar processing [9], blind source separation [10], and image processing [11,12] for instance. The conceptual basics of HOSVD will be briefly recapitulated following Ref. The conceptual basics of HOSVD will be briefly recapitulated following Ref. [7]
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