Vectorized Uncertainty Propagation and Input Probability Sensitivity Analysis

In this article we construct a theoretical and computational process for assessing Input Probability Sensitivity Analysis (IPSA) using a Graphics Processing Unit (GPU) enabled technique called Vectorized Uncertainty Propagation (VUP). VUP propagates probability distributions through a parametric computational model in a way that's computational time complexity grows sublinearly in the number of distinct propagated input probability distributions. VUP can therefore be used to efficiently implement IPSA, which estimates a model's probabilistic sensitivity to measurement and parametric uncertainty over each relevant measurement location. Theory and simulation illustrate the effectiveness of these methods.