Simulation-based design and certification is fundamentally about making decisions with uncertainty. However, minimizing uncertainty comes at a price — more testing to better define the variability in input parameters, higher fidelity analyses at a finer scale to limit the uncertainty in the physics, etc. Variability in each input parameter does not affect the uncertainty in the system response equally. Nor does every model refinement reduce the uncertainty in the system response. This paper presents a computational methodology that estimates the sensitivity of uncertainty in input variables and the sensitivity of modeling approximations to the final output. In the current age of large multi-disciplinary virtual simulation, this is useful in determining how to minimize overall uncertainty in analytical predictions. In addition, the methodology can be used to optimize for the best use of computational and testing resources to arrive at most robust predictions.

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