Before revision: With the improvement of  pattern recognition and feature extraction of Deep Neural Networks ( DNNs), more and more problems are attempted to solve from the view of images . Recently, a Reconstructive Neural Network (ReConNN) was proposed to obtain an image-based model from an analysis-based model , which can help us to solve many high frequency problems with difficult sampling, e.g. sonic wave and collision. However,  due to the slight difference between simulated images, the low-accuracy of the Convolutional Neural Network ( CNN)and poor-diversity of the Generative Adversarial Network (GAN) make the reconstruction process low-accuracy, poor-efficiency, expensive-computation and high-manpower.
After revision: With the improvement of pattern recognition and feature extraction of Deep Neural Networks ( DNNs), more and more problems are attempted to solve from the view of images . Recently, a Reconstructive Neural Network (ReConNN) was proposed to obtain an image-based model from an analysis-based model 1, 2 due to the slight difference between simulated images, the low-accuracy of the Convolutional Neural Network ( CNN)and poor-diversity of the Generative Adversarial Network (GAN) make the reconstruction process low-accuracy, poor-efficiency, expensive-computation and high-manpower.