The DOI matches its Crossref record; 62 of the 62 references it cites were found in a registry or an index.
Use of Kriging Models to Approximate Deterministic Computer Models
Abstract
1 Address all correspondences to this author. Phone/fax: (814) 865-5930/863-4128. The use of kriging models for approximation and global optimization has been steadily on the rise in the past decade. The standard approach used in the Design and Analysis of Computer Experiments (DACE) is to use an Ordinary kriging model to approximate a deterministic computer model. Universal and Detrended kriging are two alternative types of kriging models. In this paper, a description on the basics of kriging is given, highlighting the similarities and differences between these three different types of kriging models and the underlying assumptions behind each. A comparative study on the use of three different types of kriging models is then presented using six test problems. The methods of Maximum Likelihood Estimation (MLE) and Cross-Validation (CV) for model parameter estimation are compared for the three kriging model types. A one-dimension problem is first used to visualize the differences between the different models. In order to show applications in higher dimensions, four two-dimension and a 5-dimension problem are also given.
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Digital twin: values, challenges and enablers from a modeling perspective
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Hierarchical Kriging Model for Variable-Fidelity Surrogate Modeling
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Blind Kriging: A New Method for Developing Metamodels
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Digital Twin: Values, Challenges and Enablers
Rasheed, A., San, O., & Kvamsdal, T. (2019) · arXiv · doi:10.48550/arxiv.1910.01719 · Cited by 81
Metadata
- Authors
- Martin, J. D., & Simpson, T. W.
- Venue
- AIAA Journal
- Published
- 2005-04
- Volume
- 43
- Issue
- 4
- Pages
- 853-863
- ISSN
- 0001-1452 (ISSN-L)
- 1533-385X
- OpenAlex
- W2156677085
- Cited by
- 835
- References
- 62
- FWCI
- 51.27
- Type
- journal article
- Language
- en
- Open access
- closed
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