Machine Learning for Aerodynamic Uncertainty Quantification

被引：0

作者：

Liu, Dishi ^{[1
]}

Maruyama, Daigo ^{[1
]}

Goert, Stefan ^{[1
]}

机构：

[1] German Aerosp Ctr, Cologne, Germany

来源：

ERCIM NEWS | 2020年 / 122期

关键词：

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Within the framework of the project "Uncertainty Management for Robust Industrial Design in Aeronautics" (UMRIDA), funded by the European Union, several machine learning-based predictive models were compared in terms of their efficiency in estimating statistics of aerodynamic performance of aerofoils. The results show that the models based on both samples and gradients achieve better accuracy than those based solely on samples at the same computational costs.

引用

页码：20 / 21

页数：2

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