Visual Explainable Convolutional Neural Network for Aerodynamic Coefficient Prediction
Recently, aerodynamic performance analysis has been widely studied due to its importance in aircraft design. Most works adopted computational fluid dynamics (CFD) simulation to compute the aerodynamic forces, which is time consuming. To reduce the simulation time, several works proposed to use deep...
Saved in:
Main Authors: | Yanxuan Zhao, Chengwen Zhong, Fang Wang, Yueqing Wang |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2022-01-01
|
Series: | International Journal of Aerospace Engineering |
Online Access: | http://dx.doi.org/10.1155/2022/9873112 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Computer-aided cholelithiasis diagnosis using explainable convolutional neural network
by: Dheeraj Kumar, et al.
Published: (2025-02-01) -
Combining Convolutional Neural Network (CNN) and Grad-CAM for Parkinson’s Disease Prediction and Visual Explanation
by: Reyhaneh Dehghan, et al.
Published: (2024-09-01) -
A Hybrid Model for Soybean Yield Prediction Integrating Convolutional Neural Networks, Recurrent Neural Networks, and Graph Convolutional Networks
by: Vikram S. Ingole, et al.
Published: (2024-12-01) -
Effects of Flow Coefficient on Turbine Aerodynamic Performance and Loss Characteristics
by: Shaoyun Yang, et al.
Published: (2022-01-01) -
Predicting species distributions in the open ocean with convolutional neural networks
by: Morand, Gaétan, et al.
Published: (2024-09-01)