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Smart Structures and Systems Volume 17, Number 4, April 2016 , pages 611-629 DOI: https://doi.org/10.12989/sss.2016.17.4.611 |
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Self-adaptive sampling for sequential surrogate modeling of time-consuming finite element analysis |
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Seung-Seop Jin and Hyung-Jo Jung
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| Abstract | ||
| This study presents a new approach of surrogate modeling for time-consuming finite element analysis. A surrogate model is widely used to reduce the computational cost under an iterative computational analysis. Although a variety of the methods have been widely investigated, there are still difficulties in surrogate modeling from a practical point of view: (1) How to derive optimal design of experiments (i.e., the number of training samples and their locations); and (2) diagnostics of the surrogate model. To overcome these difficulties, we propose a sequential surrogate modeling based on Gaussian process model (GPM) with self-adaptive sampling. The proposed approach not only enables further sampling to make GPM more accurate, but also evaluates the model adequacy within a sequential framework. The applicability of the proposed approach is first demonstrated by using mathematical test functions. Then, it is applied as a substitute of the iterative finite element analysis to Monte Carlo simulation for a response uncertainty analysis under correlated input uncertainties. In all numerical studies, it is successful to build GPM automatically with the minimal user intervention. The proposed approach can be customized for the various response surfaces and help a less experienced user save his/her efforts. | ||
| Key Words | ||
| surrogate modeling; Gaussian process model; self-adaptive sampling; sequential Bayesian framework; time-consuming FE analysis | ||
| Address | ||
| Seung-Seop Jin and Hyung-Jo Jung: Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, Republic of Korea | ||