The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
RECURSIVE CO-KRIGING MODEL FOR DESIGN OF COMPUTER EXPERIMENTS WITH MULTIPLE LEVELS OF FIDELITY
Open accessAbstract
We consider in this paper the problem of building a fast-running approximation-also called surrogate model-of a complex computer code.The co-kriging based surrogate model is a promising tool to build such an approximation when the complex computer code can be run at different levels of accuracy.We present here an original approach to perform a multi-fidelity co-kriging model which is based on a recursive formulation.We prove that the predictive mean and the variance of the presented approach are identical to the ones of the original co-kriging model.However, our new approach allows to obtain original results.First, closed-form formulas for the universal co-kriging predictive mean and variance are given.Second, a fast cross-validation procedure for the multi-fidelity co-kriging model is introduced.Finally, the proposed approach has a reduced computational complexity compared to the previous one.The multi-fidelity model is successfully applied to emulate a hydrodynamic simulator.
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Cited by
Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling
Perdikaris, P., Raissi, M., Damianou, A., Lawrence, N. D., & Karniadakis, G. E. (2017) · Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rspa.2016.0751 · Cited by 448
Metadata
- Authors
- Le Gratiet, L., & Garnier, J.
- Venue
- International Journal for Uncertainty Quantification
- Publisher
- Begell House
- Published
- 2014
- Volume
- 4
- Issue
- 5
- Pages
- 365-386
- ISSN
- 2152-5080 (ISSN-L)
- OpenAlex
- W1972777769
- Cited by
- 388
- References
- 25
- FWCI
- 10.23
- Type
- journal article
- Language
- en
- Open access
- bronze
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- CoChat. (2026). Citation check: RECURSIVE CO-KRIGING MODEL FOR DESIGN OF COMPUTER EXPERIMENTS WITH MULTIPLE LEVELS OF FIDELITY. https://app.cochat.ai/papers/10.1615/int.j.uncertaintyquantification.2014006914
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- Le Gratiet, L., & Garnier, J. (2014). RECURSIVE CO-KRIGING MODEL FOR DESIGN OF COMPUTER EXPERIMENTS WITH MULTIPLE LEVELS OF FIDELITY. International Journal for Uncertainty Quantification. https://doi.org/10.1615/int.j.uncertaintyquantification.2014006914
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
