Vectorized Uncertainty Propagation and Input Probability Sensitivity Analysis

TL;DR

This paper introduces a GPU-accelerated vectorized method for efficient uncertainty propagation and sensitivity analysis of probabilistic models, significantly reducing computational complexity for input probability sensitivity assessments.

Contribution

It presents VUP, a novel GPU-based technique that enables fast, scalable input probability sensitivity analysis by propagating distributions with sublinear growth in computational time.

Findings

VUP reduces computation time for IPSA significantly.

The method effectively propagates probability distributions in complex models.

Simulation results confirm the efficiency and accuracy of VUP.

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.