A Unified Framework for Uncertainty and Sensitivity Analysis of Computational Models with Many Input Parameters

Computational models have found wide applications in simulating physical systems. Uncertainties in input parameters of the system can greatly influence the outputs, which are studied by Uncertainty Analysis (UA) and Sensitivity Analysis (SA). As the system becomes more complex, the number of input parameters can be large and existing methods for UA and SA are computationally intensive or prohibitive. We propose a unified framework by using a hierarchical variable selection approach to connect UA and SA with one design. By incorporating the effect hierarchy principle and the effect heredity principle, the method works well especially when the number of input parameters is large. Since the procedure requires only one design, it is economical in run size and computationally efficient. Keywords–Uncertainty analysis; Sensitivity analysis; Screening; Effect hierarchy principle; Effect heredity principle; Polynomial chaos expansions.