Reconstruction of Simulation-Based Physical Field with Limited Samples by ReConNN
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A variety of modeling techniques have been developed in the past decade to reduce the computational expense and increase the calculation accuracy. In this study, the distinctive characteristic compared to classical modeling models is "from image based model to mechanical based model (e.g. stress, strain, and deformation)". In such framework, a neural network architecture named ReConNN is proposed and the ReConNN mainly contains two neural networks that are CNN and GAN. A classical topology optimization is considered as an experimental example, and the CNN is employed to construct the mapping between contour images during topology optimization and compliance. Subsequently, the GAN is utilized to generate more contour images to improve the reconstructed model. Finally, the Lagrange polynomial is applied to complete the reconstruction. However, typical CNN architectures are commonly applied to classification problems, which appear powerless handling with regression of images for simulation problems. Meanwhile, the existing GAN architectures are insufficient to generate high-accuracy "pseudo contour images". Therefore, a Convolution in Convolution (CIC) architecture and a Convolutional AutoEncoder based on Wasserstein Generative Adversarial Network (WGAN-CAE) architecture are suggested. Specially, extensive experiments and comparisons with existing architectures of CNN and GAN demonstrate that the CIC is highly accurate and corresponding computational cost also can be significantly reduced when handling the regression problem of contour images, and the WGAN-CAE achieves significant improvements on generating contour images. The results demonstrate that the proposed ReConNN has a potential capability to reconstruct physical field for further researches, e.g. optimization.
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