General Introduction to Surrogate Model-Based Approaches to UQ
| Autor: | Dishi Liu, Daigo Maruyama, Stefan Görtz |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Radial Basis Function
021103 operations research business.industry Computer science 0211 other engineering and technologies Surrogate 02 engineering and technology Aerodynamics Computational fluid dynamics Parameter space 01 natural sciences 010101 applied mathematics Bayesian statistics Kriging Surrogate model Modelling methods Applied mathematics Radial basis function Uncertainty Quantification 0101 mathematics business |
| Zdroj: | Uncertainty Management for Robust Industrial Design in Aeronautics ISBN: 9783319777665 |
| Popis: | This chapter introduces two popular surrogate modeling methods which can be used to quantify uncertainties such as statistics of the aerodynamic coefficients from scattered data obtained by computational fluid dynamics (CFD) simulations. One is Kriging, which is able not only to interpolate predicted data but also to provide statistical information at unsampled locations in the parameter space based on Bayesian statistics. The other one is the radial basis function (RBF) method. The RBF method is also a powerful nonlinear interpolation method which exactly interpolates the samples, and its various radial basis function types support the interpolated values locally or globally when appropriately selected. Both methods can make use of gradient information, if available, to improve the model accuracy. |
| Databáze: | OpenAIRE |
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