  Designing composite materials as per the application requirements is
fundamentally a challenging and time consuming task. Here we report the
development of a deep neural network based computational framework capable of
solving the forward (predictive) as well as inverse (generative) design
problem. The predictor model is based on the popular convolution neural network
architecture and trained with the help of finite element simulations. Further,
the developed property predictor model is used as a feedback mechanism in the
neural network based generator model. The proposed predictive-generative model
can be used to obtain the micro-structure for maximization of particular
elastic properties as well as for specified elastic constants. One of the major
hurdle for deployment of the deep learning techniques in composite material
design is the intensive computational resources required to generate the
training data sets. To this end, a novel data augmentation scheme is presented.
The application of data augmentation scheme results in significant saving of
computational resources in the training phase. The proposed data augmentation
approach is general and can be used in any setting involving the periodic
micro-structures. The efficacy of the predictive-generative model is
demonstrated through various examples. It is envisaged that the developed model
will significantly reduce the cost and time associated with the composite
material designing process for advanced applications.
