Fast Uncertainty Quantification by Sparse Data Learning From Multiphysics Systems

Data learning is a burgeoning area; here, we refer to it as a gorgeous blend of scientific computing and high-dimensional statistics. In this article, we propose a generalized polynomial chaos expansion (gPCE)-based surrogate model, with a sparse dataset, for multiphysics problems with associated uncertainty. This data-driven method avoids the dilemma of traditional intrusive methods, which usually require tremendous revisions over the backend source code. To circumvent the curse of dimensionality in uncertainty quantification, we perform sparse data learning for the relationship between the system output and the gPCE bases. Numerical experiments showcase that the surrogate model exhibits superior fast performance in system-output prediction, statistical-feature extraction, and sensitivity analysis of the 3-D multiphysics problems.