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 com...

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Bibliographic Details
Published in:arXiv.org 2019-08
Main Authors: Vanslette, Kevin, Alanqari, Arwa, Al-awwad, Zeyad, Youcef-Toumi, Kamal
Format: Article
Language:English
Subjects:
Computer simulation
Computing time
Graphics processing units
Parameter sensitivity
Propagation
Sensitivity analysis
Statistical analysis
Uncertainty
Online Access:Get full text
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Summary: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.
ISSN:2331-8422