Efficient response surface methods based on generic surrogate models
For engineers running expensive computer experiments (e.g., CFD), this method reduces the number of costly simulations needed for accurate surrogate models.
The paper introduces a method that uses statistical shape models to build generic surrogate models for a class of related problems, combining them with sample data in a variable fidelity framework to achieve globally accurate interpolation with fewer costly evaluations. Demonstrated on an aerodynamic test case, it significantly improves approximation quality.
Surrogate models are used for global approximation of responses generated by expensive computer experiments like CFD applications. In this paper, we make use of structural similarities which are shared by a class of related problems. We identify these structures by applying statistical shape models. They are used to build a generic surrogate model approximation to sample data of a new problem of the same class. In a variable fidelity framework the generic surrogate model is combined with the sample data to generate an efficient and globally accurate interpolation model, which requires less costly sample evaluations than ordinary response surface methods. We demonstrate our method with an aerodynamic test case and show that it significantly improves the approximation quality.