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We present a novel probabilistic approach for generating multi-fidelity data while accounting for errors inherent in both low- and high-fidelity data. In this approach a graph Laplacian constructed from the low-fidelity data is used to define a multi
Externí odkaz:
http://arxiv.org/abs/2409.08211
Autor:
Pinti, Orazio, Oberai, Assad A.
Low-fidelity data is typically inexpensive to generate but inaccurate. On the other hand, high-fidelity data is accurate but expensive to obtain. Multi-fidelity methods use a small set of high-fidelity data to enhance the accuracy of a large set of l
Externí odkaz:
http://arxiv.org/abs/2304.04862
These notes were compiled as lecture notes for a course developed and taught at the University of the Southern California. They should be accessible to a typical engineering graduate student with a strong background in Applied Mathematics. The main o
Externí odkaz:
http://arxiv.org/abs/2301.00942
Autor:
Pinti, Orazio1 (AUTHOR) pinti@usc.edu, Oberai, Assad A.1 (AUTHOR)
Publikováno v:
Scientific Reports. 10/3/2023, Vol. 13 Issue 1, p1-13. 13p.
Akademický článek
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