Mathematical Considerations on Randomized Orthgonal Decomposition Method for Developing Twin Data Models
Numerical Analysis
2024-10-07 v1 Numerical Analysis
Abstract
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.
Cite
@article{arxiv.2410.02813,
title = {Mathematical Considerations on Randomized Orthgonal Decomposition Method for Developing Twin Data Models},
author = {Diana A. Bistrian},
journal= {arXiv preprint arXiv:2410.02813},
year = {2024}
}
Comments
11 pages, 3 figures. arXiv admin note: substantial text overlap with arXiv:2206.08659