Deep Learning Model on 2-Dimensional Image Data using Convolutional Autoencoder and Fully Connected Neural Networks: Application to Computational Fluid Dynamics
Abstract:This study proposes a deep-learning-based image prediction meta-modeling method to develop an image-based approximate optimized design using 2D image data. An image-based meta-model is generated with an autoencoder (AE) and fully connected neural networks (FNN). To create this meta-model, we suggested three methods as FNN-based AE, convolutional autoencoder (CAE) based on convolution neural networks (CNN), and hybrid-convolutional autoencoder (H-CAE) combining the FNN and CAE. To verify the proposed methods, w… Show more
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