Computationally Efficient Imprecise Uncertainty Propagation

Modeling uncertainty through probabilistic representation in engineering design is common and important to decision making that considers risk. However, representations of uncertainty often ignore elements of “imprecision” that may limit the robustness of decisions. Furthermore, current approaches that incorporate imprecision suffer from computational expense and relatively high solution error. This work presents a method that allows imprecision to be incorporated into design scenarios while providing computational efficiency and low solution error for uncertainty propagation. The work draws on an existing method for representing imprecision and integrates methods for sparse grid numerical integration, resulting in the computationally efficient imprecise uncertainty propagation (CEIUP) method. This paper presents details of the method and demonstrates the effectiveness on both numerical case studies, and a thermocouple performance problem found in the literature. Results for the numerical case studies, in most cases, demonstrate improvements in both computational efficiency and solution accuracy for varying problem dimension and variable interaction when compared to optimized parameter sampling (OPS). For the thermocouple problem, similar behavior is observed when compared to OPS. The paper concludes with an overview of design problem scenarios in which CEIUP is the preferred method and offers opportunities for extending the method.