A Generative Modeling Framework for Statistical Link Analysis Based on Sparse Data

This paper proposes a novel strategy for creating generative models of stochastic link responses starting from limited available data. Whereas state-of-the-art techniques, e.g., based on generalized polynomial chaos expansions, require a considerable amount of (expensive) input data, here we start from a small set of “training” responses. These responses are obtained either from simulations or mea...