  We propose and demonstrate the use of a model-assisted generative adversarial
network (GAN) to produce fake images that accurately match true images through
the variation of the parameters of the model that describes the features of the
images. The generator learns the model parameter values that produce fake
images that best match the true images. Two case studies show excellent
agreement between the generated best match parameters and the true parameters.
The best match model parameter values can be used to retune the default
simulation to minimize any bias when applying image recognition techniques to
fake and true images. In the case of a real-world experiment, the true images
are experimental data with unknown true model parameter values, and the fake
images are produced by a simulation that takes the model parameters as input.
The model-assisted GAN uses a convolutional neural network to emulate the
simulation for all parameter values that, when trained, can be used as a
conditional generator for fast fake-image production.
