科研之友
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摘要
Predicting or measuring the output of complex systems is an important and challenging part of many areas of science. If multiple observations are required for parameter studies and optimization, accurate, computationally intensive predictions or expensive experiments are intractable. This paper looks at the use of Gaussian-process-based correlations to correct simple computer models with sparse data from physical experiments or more complex computer models. In essence, physics-based computer codes and experiments are replaced by fast problem-specific statistics-based codes. Two aerodynamic design examples are presented. First, a cheap two-dimensional potential flow solver is calibrated to represent the flow over the wing of an unmanned air vehicle. The rear wing of a racing car is then optimized using rear-wing simulations calibrated to include the effects of the flow over the whole car.
- 出版日期2010-8-13
