基于复合神经网络的多源气动数据建模
Multi-fidelity aerodynamic data analysis by using composite neural network
朱星谕 1梅立泉1
作者信息
- 1. 西安交通大学 数学与统计学院,陕西 西安 710049
- 折叠
摘要
将深度学习方法应用至气动数据建模,能够解决传统建模方法效率低、代价高的问题,具有重要的现实意义.基于复合神经网络模型对多源气动数据进行学习,利用低精度数据辅助高精度数据进行预测.与不同网络模型进行对比,验证了文中提出的复合神经网络在气动数据建模中表现优良,且泛化能力较好.
Abstract
Applying deep learning to aerodynamic data modeling has important practical significance.In this paper,the composite neural network is applied to the aerodynamics,making full use of the different characteristics of high and low-fidelity aerodynamic data.Multi-fidelity analysis technique is also used to analyze the correlation between the two types of data so as to establish the composite neural network.The experimental results show that the learning of multi-fidelity aerodynamic data based on the composite neural network model can better capture the mapping rela-tionship between the aerodynamic input and the output data.And after comparing with the single neural network,it is verified that the present model has excellent performance in the regression modeling of aerodynamic data.
关键词
气动数据建模/深度神经网络/复合神经网络Key words
aerodynamic data modeling/deep neural network/composite neural network引用本文复制引用
出版年
2024
NETL
NSTL
万方数据