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|Title:||An adaptive RBF-HDMR modeling approach under limited computational budget||Authors:||Liu, Haitao
|Keywords:||Engineering::Computer science and engineering||Issue Date:||2017||Source:||Liu, H., Hervas, J.-R., Ong, Y.-S., Cai, J., & Wang, Y. (2018). An adaptive RBF-HDMR modeling approach under limited computational budget. Structural and Multidisciplinary Optimization, 57(3), 1233-1250. doi:10.1007/s00158-017-1807-0||Journal:||Structural and Multidisciplinary Optimization||Abstract:||The metamodel-based high-dimensional model representation (e.g., RBF-HDMR) has recently been proven to be very promising for modeling high dimensional functions. A frequently encountered scenario in practical engineering problems is the need of building accurate models under limited computational budget. In this context, the original RBF-HDMR approach may be intractable due to the independent and successive treatment of the component functions, which translates in a lack of knowledge on when the modeling process will stop and how many points (simulations) it will cost. This article proposes an adaptive and tractable RBF-HDMR (ARBF-HDMR) modeling framework. Given a total of Nmax points, it first uses Nini points to build an initial RBF-HDMR model for capturing the characteristics of the target function f, and then keeps adaptively identifying, sampling and modeling the potential cuts with the remaining Nmax − Nini points. For the second-order ARBF-HDMR, Nini ∈ [2n + 2,2n2 + 2] not only depends on the dimensionality n but also on the characteristics of f. Numerical results on nine cases with up to 30 dimensions reveal that the proposed approach provides more accurate predictions than the original RBF-HDMR with the same computational budget, and the version that uses the maximin sampling criterion and the best-model strategy is a recommended choice. Moreover, the second-order ARBF-HDMR model significantly outperforms the first-order model; however, if the computational budget is strictly limited (e.g., 2n + 1 < Nmax ≪ 2n2 + 2), the first-order model becomes a better choice. Finally, it is noteworthy that the proposed modeling framework can work with other metamodeling techniques.||URI:||https://hdl.handle.net/10356/139027||ISSN:||1615-147X||DOI:||10.1007/s00158-017-1807-0||Rights:||© 2017 Springer-Verlag GmbH Germany. All rights reserved.||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||SCSE Journal Articles|
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