Neural Network Models of Phase Field Simulations

We explore the potential for neural networks (NNs) to predict two aspects of microstructure evolution, as represented in simple phase field simulations. First, we train NNs to predict the evolution of microstructures under pre-specified, spatially and temporally varying temperature and bias fields. This problem is amenable to solution by existing NN training methods because each input maps to a unique output. Next, we train NNs to solve the inverse problem, namely: what externally applied temperature and bias fields are required to guide microstructure evolution to a pre-specified target state? In this problem, inputs do not map to unique outputs, necessitating an innovative approach to NN training. Our work suggests that NNs may be useful for finding optimal processing parameters for achieving a desired microstructure.