Automatic Selection of High-Fidelity Models and Surrogates for Uncertainty Analysis
Surrogate models (SMs) with distinct features developed in the last decade provide an efficient substitute to the high-fidelity physical simulations. However, it is generally challenging to quantify the accuracy of surrogate models in a region of interest and examine reliability in the overall domain without additional system evaluations. This paper introduces a new scheme, automated adaptive model selection, to adaptively construct and quantify a set of surrogate models and automatically switch between the surrogates and a high-fidelity code. Cross-validation approaches, such as KFold, Leave One Out (LOO) and Leave P Out (LPO), are employed to assess the global accuracy of the surrogates. The switching strategy is based on local validation metrics, crowding distance, that are able to provide an assessment on the goodness of the prediction made by the surrogates. This decision metric decides whether the surrogates are good enough for a certain realization or whether the high-fidelity code needs to be executed. This new scheme has been implemented in Risk Analysis in Virtual ENvironment (RAVEN) software, which can be employed to accelerate the Probabilistic Risk Assessment (PRA) and Uncertainty Quantification (UQ) processes.