A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf
.
Multi-Fidelity Aerodynamic Data Fusion with a Deep Neural Network Modeling Method
2020
Entropy
To generate more high-quality aerodynamic data using the information provided by different fidelity data, where low-fidelity aerodynamic data provides the trend information and high-fidelity aerodynamic data provides value information, we applied a deep neural network (DNN) algorithm to fuse the information of multi-fidelity aerodynamic data. We discuss the relationships between the low-fidelity and high-fidelity data, and then we describe the proposed architecture for an aerodynamic data
doi:10.3390/e22091022
pmid:33286791
pmcid:PMC7597116
fatcat:ouqcgrzzcnf5pcnqotldvrp4um