Data Correction by a Generative Model with an Encoder and its Application to Structure Design

An alternative training model is proposed for adversarial networks to correct a slightly defective data. Generator is first acquired by classical Generative Adversarial Networks, where the discriminator is trained only by feasible data. Then, both an encoder as the inverse mapping of the generator and a classifier which judges a feasibility of a generated data, are trained to lead the generator to correct an infeasible data by the minimum modification. The proposed method is applied to a housing member placement problem to satisfy every constraint for earthquake resistance, and evaluated by a rigorous structural calculation.