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Layout designs are encountered in a variety of fields. For problems with many design degrees of freedom, efficiency of design methods becomes a major concern. In recent years, machine learning methods such as artificial neural networks have been used increasingly to speed up the design process. A main issue of many such approaches is the need for a large corpus of training data that are generated using high-dimensional simulations. The high computational cost associated with training data generation largely diminishes the efficiency gained by using machine learning methods. In this work, an adaptive artificial neural network-based generative design approach is proposed and developed. This method uses a generative adversarial network to generate design candidates and thus the number of design variables is greatly reduced. To speed up the evaluation of the objective function, a convolutional neural network is constructed as the surrogate model for function evaluation. The inverse design is carried out using the genetic algorithm in conjunction with two neural networks. A novel adaptive learning and optimization strategy is proposed, which allows the design space to be effectively explored for the search for optimal solutions. As such the number of training data needed is greatly reduced. The performance of the proposed design method is demonstrated on two heat source layout design problems. In both problems, optimal designs have been obtained. Compared with several existing approaches, the proposed approach has the best performance in terms of accuracy and efficiency.

Topology design which optimizes the materials or components distributions in a selected domain to achieve certain objectives has been widely applied in engineering field such as transportation vehicles or architectural designs. In structural mechanics, topology optimization (TO) provides systematic approach to optimize the topology of the structure so that desired material properties are fulfilled. However the method requires the iterative calculation of objective function, which involves computationally expensive numerical simulation such as finite element method (FEM) calculation. As the computational cost for the process scales exponentially with the domain or mesh sizes, constant development is performed to improve the efficiency of the method. In recent years, various deep learning-based methods have been developed to accelerate the design process. Although the existing applications of deep learning models could accelerate the design process through different approaches, most of them suffer from the issue of transferability and the requirement of large amount of training data. Since the training data is required to be generated through numerical simulation itself, the overall efficiency of those methods is reduced. In this thesis, three major ANN-based methods are developed with the ultimate objective of accelerating the traditional topology design process, while also taking into account the transferability of the networks and the training data generation process. In the first method, an inverse design model is built with a combination of a surrogate model based on Convolutional Neural Network (CNN), and a generative model, Deep Convolutional Generative Adversarial Network (DCGAN). The method is developed to solve inverse design problem in a short time, by generating designs that satisfy desired mechanical properties, while also subjected to prescribed geometrical constraint. The design of microstructural materials to achieve specified effective compliance tens

Due to their designable properties, microstructural materials have emerged as an important class of materials that have the potential for used in a variety of applications. The design of such materials is challenged by the multifunctionality requirements and various constraints stemmed from manufacturing limitations and other practical considerations. Traditional design methods such as those based on topological optimization techniques rely heavily on high-dimensional physical simulations and can be inefficient. In addition, it is difficult to impose geometrical constraints in those methods. In this work, we propose a deep learning model based on deep convolutional generative adversarial network (DCGAN) and convolutional neural network (CNN) for the design of microstructural materials. The DCGAN is used to generate design candidates that satisfy geometrical constraints and the CNN is used as a surrogate model to link the microstructure to its properties. Once trained, the two networks are combined to form the design network which is utilized to for the inverse design. The advantages of the method include its high efficiency and the simplicity in handling geometrical constraints. In addition, no high-dimensional sensitivity simulations are required. The performance of the method is demonstrated on the design of microstructural materials with desired compliance tensor, subject to specified geometrical constraints. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.