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arXiv · 1908.11246

Vectorized Uncertainty Propagation and Input Probability Sensitivity Analysis

Kevin Vanslette·Arwa Alanqari·Zeyad Al-awwad·Kamal Youcef-Toumi
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Abstract

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.

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Kevin Vanslette, Arwa Alanqari, Zeyad Al-awwad, Kamal Youcef-Toumi. 2019-08-29. Vectorized Uncertainty Propagation and Input Probability Sensitivity Analysis. https://arxiv.org/abs/1908.11246

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