Parametric Interpolation Model Order Reduction on Grassmann Manifolds by Parallelization

Based on Riemannian geometry of Grassmann manifolds and discrete Laguerre polynomials, we propose a parametric interpolation parallel MOR method for discrete-time parametric systems. First, a block discrete Fourier transform-based (BDFT) parallel strategy is presented to construct the local basis matrices, which achieves two level acceleration by parallelization. Further, using the retractions and vector transports on Grassmann manifolds, the basis matrix for a new parameter is obtained by interpolating the local bases on the tangent spaces at the different reference points. Finally, a numerical example is used to demonstrate the efficiency of our method.

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Publication Details

Journal
IEEE Transactions on Circuits & Systems II Express Briefs
Published
2024-09-13
DOI
https://doi.org/10.1109/tcsii.2024.3460171
Citations
5
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
1.41

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article

Parametric Interpolation Model Order Reduction on Grassmann Manifolds by Parallelization

Peter Benner, Zhen Li, Kang-Li Xu
5 citations
IEEE Transactions on Circuits & Systems II Express Briefs
Model Reduction and Neural Networks
1.41
article

Parametric Interpolation Model Order Reduction on Grassmann Manifolds by Parallelization

Peter Benner, Zhen Li, Kang-Li Xu
article en
5 citations

Abstract

Based on Riemannian geometry of Grassmann manifolds and discrete Laguerre polynomials, we propose a parametric interpolation parallel MOR method for discrete-time parametric systems. First, a block discrete Fourier transform-based (BDFT) parallel strategy is presented to construct the local basis matrices, which achieves two level acceleration by parallelization. Further, using the retractions and vector transports on Grassmann manifolds, the basis matrix for a new parameter is obtained by interpolating the local bases on the tangent spaces at the different reference points. Finally, a numerical example is used to demonstrate the efficiency of our method.

IEEE Transactions on Circuits & Systems II Express BriefsVol. 72(1)
Xi'an University of Science and Technology (CN), Max Planck Institute for Dynamics of Complex Technical Systems (DE), Xi'an Jiaotong University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 19%
Model Reduction and Neural Networks
1.41
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Parametric Interpolation Model Order Reduction on Grassmann Manifolds by Parallelization — Peter Benner, Zhen Li, et al. · IEEE Transactions on Circuits & Systems II Express Briefs (2024) | TGRS Research Map | TGRS