https://github.com/himanshuvnm/limited-data-acquisition-julia

This research work is about Limited Data Acquisition for the real life physical experiment of fluid flow across cylinder based on Kernelized Extended Dynamic Mode Decomposition by incorporating Gaussian Random Matrix Theory and Laplacian Kernel Function Hilbert space.
https://github.com/himanshuvnm/limited-data-acquisition-julia

dynamic-mode-decomposition gaussian-random-vector julia-language koopman-operator-theory laplacian-kernel random-matrix-theory reproducing-kernel-hilbert-space

Last synced: 2 days ago
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This research work is about Limited Data Acquisition for the real life physical experiment of fluid flow across cylinder based on Kernelized Extended Dynamic Mode Decomposition by incorporating Gaussian Random Matrix Theory and Laplacian Kernel Function Hilbert space.

• Host: GitHub
• URL: https://github.com/himanshuvnm/limited-data-acquisition-julia
• Owner: himanshuvnm
• Created: 2024-03-10T10:03:21.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2024-03-13T13:05:26.000Z (7 months ago)
• Last Synced: 2024-09-24T22:04:24.647Z (2 days ago)
• Topics: dynamic-mode-decomposition, gaussian-random-vector, julia-language, koopman-operator-theory, laplacian-kernel, random-matrix-theory, reproducing-kernel-hilbert-space
• Language: Python
• Homepage:
• Size: 8.93 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
The above codes are given in JULIA for executing Dyanamic Mode Decomposition with Limited Data Acquisition. The paper for this code corresponds to https://arxiv.org/abs/2312.12630 which is entitled as 'Data-driven discovery with Limited Data Acquisition for fluid flow across cylinder'.