8/31/2023 0 Comments Sample of tessellation art![]() ![]() However, there is a lack of exhaustive comparisons between alternative adaptive techniques suggested in the literature. Įxisting reviews have offered an overview of existing adaptive sampling strategies, focusing either on space-filling techniques, or on adaptive design. These approaches have received increasing attention in recent years, from the 1990s for neural networks and later for support vector machines. Space-filling techniques aim to spread the samples evenly in an iterative manner, whereas using adaptive sampling techniques, new samples are designed based on information extracted during previous iterations in order to place them in locations of high interest. To circumvent the limitation of one-shot sampling, sequential sampling techniques have been proposed since the 1950s, including two families, space-filling and adaptive design, as illustrated in Fig. ![]() Its interpolative and stochastic properties make it very attractive, and so it is referred to as the most intensively investigated metamodel in Jiang et al. Kriging, originally developed by Krige for use in geostatistics, has been expanded to computer experiments with both deterministic and random nature. , a large variety of adaptive sampling techniques have been proposed for kriging. Within this context, since the groundbreaking work of Sacks et al. Moreover, because evaluating the black-box function may be computationally demanding for engineering applications,Ī further goal in the process is to reduce the number of samples as much as possible, while generating a proficient surrogate model. Hence, the accuracy of the resulting metamodel is highly dependent on the samples. The relevant black-box function is thereafter evaluated at each sample point and results are exploited to fit the surrogate model over the whole parametric domain. Samples are distributed in a user-defined parametric space. All experiments described herein are replicable using a provided open source toolbox.Ī general metamodel process is schematized in Fig. In order to facilitate the entry into adaptive sampling a guide is provided. 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 this context commonly applied strategies are compared with regards to their characteristics and approximation capabilities. 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. A review of adaptive schemes for kriging proposed in the literature is presented in this article. These techniques aim to find pertinent points in an iterative manner based on information extracted from the current metamodel. Hence, in order to build proficient kriging models with as few samples as possible adaptive sampling strategies have gained considerable attention. However its prediction quality is highly dependent on the size and distribution of the given training points. In recent years kriging has emerged as a widely applied metamodeling technique for resource-intensive computational experiments. Metamodels aim to approximate characteristics of functions or systems from the knowledge extracted on only a finite number of samples. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |