  A variety of modeling techniques have been developed in the past decade to
reduce the computational expense and improve the accuracy of modeling. In this
study, a new framework of modeling is suggested. Compared with other popular
methods, a distinctive characteristic is "from image based model to analysis
based model (e.g. stress, strain, and deformation)". In such a framework, a
reconstruction neural network (ReConNN) model designed for simulation-based
physical field's reconstruction is proposed. The ReConNN contains two submodels
that are convolutional neural network (CNN) and generative adversarial net-work
(GAN). The CNN is employed to construct the mapping between contour images of
physical field and objective function. Subsequently, the GAN is utilized to
generate more images which are similar to the existing contour images. Finally,
Lagrange polynomial is applied to complete the reconstruction. However, the
existing CNN models are commonly applied to the classification tasks, which
seem to be difficult to handle with regression tasks of images. Meanwhile, the
existing GAN architectures are insufficient to generate high-accuracy "pseudo
contour images". Therefore, a ReConNN model based on a Convolution in
Convolution (CIC) and a Convolutional AutoEncoder based on Wasserstein
Generative Adversarial Network (WGAN-CAE) is suggested. To evaluate the
performance of the proposed model representatively, a classical topology
optimization procedure is considered. Then the ReConNN is utilized to the
reconstruction of heat transfer process of a pin fin heat sink. It demonstrates
that the proposed ReConNN model is proved to be a potential capability to
reconstruct physical field for multidisciplinary, such as structural
optimization.
