  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.
