Publications
Resource allocation using quantification of margins and uncertainty
There is an increasing need to assess the performance of high consequence systems using a modeling and simulation based approach. Central to this approach are the need to quantify the uncertainties present in the system and to compare the system response to an expected performance measure. At Sandia National Laboratories, this process is referred to as quantification of margins and uncertainties or QMU. Depending on the outcome of the assessment, there might be a need to increase the confidence in the predicted response of a system model; thus a need to understand where resources need to be allocated to increase this confidence. This paper examines the problem of resource allocation done within the context of QMU. An optimization based approach to solving the resource allocation is considered and sources of aleatoric and epistemic uncertainty are included in the calculations.