Neural Information Processing Systems Foundation (NeurIPS 2019), 2019
We propose a GAN based approach to solve inverse problems which have non-differentiable or even black-box forward relations.
The idea is to find solutions via an adversarial game where the generator has to propose new samples and the discriminator has to assess the quality of the samples with respect to the forward relation $f$. However, instead of attempting to approximate $f$ directly, the discriminator only has to solve a binary classification task in local regions populated by the generated samples. We demonstrate the efficacy of our approach by applying it to an artificially generated topology optimization problem. We show that our method leads to similar results like more traditional topology optimization methods.