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Graph Laplacian-based spectral multi-fidelity modeling.
- Source :
-
Scientific Reports . 10/3/2023, Vol. 13 Issue 1, p1-13. 13p. - Publication Year :
- 2023
-
Abstract
- Low-fidelity data is typically inexpensive to generate but inaccurate, whereas high-fidelity data is accurate but expensive. To address this, multi-fidelity methods use a small set of high-fidelity data to enhance the accuracy of a large set of low-fidelity data. In the approach described in this paper, this is accomplished by constructing a graph Laplacian from the low-fidelity data and computing its low-lying spectrum. This is used to cluster the data and identify points closest to the cluster centroids, where high-fidelity data is acquired. Thereafter, a transformation that maps every low-fidelity data point to a multi-fidelity counterpart is determined by minimizing the discrepancy between the multi- and high-fidelity data while preserving the underlying structure of the low-fidelity data distribution. The method is tested with problems in solid and fluid mechanics. By utilizing only a small fraction of high-fidelity data, the accuracy of a large set of low-fidelity data is significantly improved. [ABSTRACT FROM AUTHOR]
- Subjects :
- *SOLID mechanics
*FLUID mechanics
*DATA distribution
*SPECTRAL imaging
Subjects
Details
- Language :
- English
- ISSN :
- 20452322
- Volume :
- 13
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Scientific Reports
- Publication Type :
- Academic Journal
- Accession number :
- 172754477
- Full Text :
- https://doi.org/10.1038/s41598-023-43719-1