Details
| Original language | English |
|---|---|
| Pages (from-to) | 2689-2747 |
| Number of pages | 59 |
| Journal | Archives of Computational Methods in Engineering |
| Volume | 28 |
| Issue number | 4 |
| Early online date | 18 Aug 2020 |
| Publication status | Published - Jun 2021 |
Abstract
Metamodels aim to approximate characteristics of functions or systems from the knowledge extracted on only a finite number of samples. In recent years kriging has emerged as a widely applied metamodeling technique for resource-intensive computational experiments. However its prediction quality is highly dependent on the size and distribution of the given training points. Hence, in order to build proficient kriging models with as few samples as possible adaptive sampling strategies have gained considerable attention. These techniques aim to find pertinent points in an iterative manner based on information extracted from the current metamodel. A review of adaptive schemes for kriging proposed in the literature is presented in this article. The objective is to provide the reader with an overview of the main principles of adaptive techniques, and insightful details to pertinently employ available tools depending on the application at hand. In this context commonly applied strategies are compared with regards to their characteristics and approximation capabilities. In light of these experiments, it is found that the success of a scheme depends on the features of a specific problem and the goal of the analysis. In order to facilitate the entry into adaptive sampling a guide is provided. All experiments described herein are replicable using a provided open source toolbox.
ASJC Scopus subject areas
- Computer Science(all)
- Computer Science Applications
- Mathematics(all)
- Applied Mathematics
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
In: Archives of Computational Methods in Engineering, Vol. 28, No. 4, 06.2021, p. 2689-2747.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - State-of-the-Art and Comparative Review of Adaptive Sampling Methods for Kriging
AU - Fuhg, Jan N.
AU - Fau, Amélie
AU - Nackenhorst, Udo
N1 - Funding Information: The first author acknowledges the financial support from the Deutsche Forschungsgemeinschaft under Germanys’ Excellence Strategy within the Cluster of Excellence PhoenixD (EXC 2122, Project ID 390833453). Open Access funding provided by Projekt DEAL. Acknowledgements
PY - 2021/6
Y1 - 2021/6
N2 - Metamodels aim to approximate characteristics of functions or systems from the knowledge extracted on only a finite number of samples. In recent years kriging has emerged as a widely applied metamodeling technique for resource-intensive computational experiments. However its prediction quality is highly dependent on the size and distribution of the given training points. Hence, in order to build proficient kriging models with as few samples as possible adaptive sampling strategies have gained considerable attention. These techniques aim to find pertinent points in an iterative manner based on information extracted from the current metamodel. A review of adaptive schemes for kriging proposed in the literature is presented in this article. The objective is to provide the reader with an overview of the main principles of adaptive techniques, and insightful details to pertinently employ available tools depending on the application at hand. In this context commonly applied strategies are compared with regards to their characteristics and approximation capabilities. In light of these experiments, it is found that the success of a scheme depends on the features of a specific problem and the goal of the analysis. In order to facilitate the entry into adaptive sampling a guide is provided. All experiments described herein are replicable using a provided open source toolbox.
AB - Metamodels aim to approximate characteristics of functions or systems from the knowledge extracted on only a finite number of samples. In recent years kriging has emerged as a widely applied metamodeling technique for resource-intensive computational experiments. However its prediction quality is highly dependent on the size and distribution of the given training points. Hence, in order to build proficient kriging models with as few samples as possible adaptive sampling strategies have gained considerable attention. These techniques aim to find pertinent points in an iterative manner based on information extracted from the current metamodel. A review of adaptive schemes for kriging proposed in the literature is presented in this article. The objective is to provide the reader with an overview of the main principles of adaptive techniques, and insightful details to pertinently employ available tools depending on the application at hand. In this context commonly applied strategies are compared with regards to their characteristics and approximation capabilities. In light of these experiments, it is found that the success of a scheme depends on the features of a specific problem and the goal of the analysis. In order to facilitate the entry into adaptive sampling a guide is provided. All experiments described herein are replicable using a provided open source toolbox.
UR - http://www.scopus.com/inward/record.url?scp=85089828606&partnerID=8YFLogxK
U2 - 10.1007/s11831-020-09474-6
DO - 10.1007/s11831-020-09474-6
M3 - Article
AN - SCOPUS:85089828606
VL - 28
SP - 2689
EP - 2747
JO - Archives of Computational Methods in Engineering
JF - Archives of Computational Methods in Engineering
SN - 1134-3060
IS - 4
ER -