Mathematical Considerations on Randomized Orthgonal Decomposition Method for Developing Twin Data Models

Fuente: arXiv
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Main Author: Bistrian, Diana A.
Format: Preprint
Published: 2024
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author Bistrian, Diana A.
author_facet Bistrian, Diana A.
contents This paper introduces the approach of Randomized Orthogonal Decomposition (ROD) for producing twin data models in order to overcome the drawbacks of existing reduced order modelling techniques. When compared to Fourier empirical decomposition, ROD provides orthonormal shape modes that maximize their projection on the data space, which is a significant benefit. A shock wave event described by the viscous Burgers equation model is used to illustrate and evaluate the novel method. The new twin data model is thoroughly evaluated using certain criteria of numerical accuracy and computational performance.
format Preprint
id arxiv_https___arxiv_org_abs_2410_02813
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Mathematical Considerations on Randomized Orthgonal Decomposition Method for Developing Twin Data Models
Bistrian, Diana A.
Numerical Analysis
93A30, 70K75, 65C20
This paper introduces the approach of Randomized Orthogonal Decomposition (ROD) for producing twin data models in order to overcome the drawbacks of existing reduced order modelling techniques. When compared to Fourier empirical decomposition, ROD provides orthonormal shape modes that maximize their projection on the data space, which is a significant benefit. A shock wave event described by the viscous Burgers equation model is used to illustrate and evaluate the novel method. The new twin data model is thoroughly evaluated using certain criteria of numerical accuracy and computational performance.
title Mathematical Considerations on Randomized Orthgonal Decomposition Method for Developing Twin Data Models
topic Numerical Analysis
93A30, 70K75, 65C20
url https://arxiv.org/abs/2410.02813