Adaptive Multi-fidelity Metamodels for UQ using Noisy CFD Data

An adaptive N-fidelity approach to metamodeling from noisy data is presented for uncertainty quantification and design-space exploration. Computational fluid dynamics (CFD) simulations with different numerical accuracy provides metamodel training sets affected by unavoidable numerical noise. The N-fidelity approximation is built by an additive correction of a low-fidelity metamodel with metamodels of differences between higher-fidelity levels whose hierarchy needs to be provided. The approach encompasses two core metamodeling techniques, namely: i) stochastic radial-basis functions and ii) Gaussian process. The adaptivity stems from the sequential training procedure and the auto-tuning capabilities of the metamodels. The method is demonstrated for two CFD-based problems.