Efficient sampling for polynomial chaos-based uncertainty quantification and sensitivity analysis using weighted approximate Fekete points

TL;DR

This paper introduces weighted approximate Fekete points (WAFP) as an efficient sampling method for polynomial chaos-based uncertainty quantification and sensitivity analysis, demonstrating its effectiveness in a cardiovascular model.

Contribution

The paper proposes a novel WAFP sampling method for polynomial chaos expansion, improving computational efficiency in UQ and SA for physiological models.

Findings

WAFP-based polynomial chaos matches Monte Carlo in uncertainty quantification.

WAFP significantly reduces computational cost compared to traditional methods.

The approach effectively identifies influential model inputs and interactions.

Abstract

Performing uncertainty quantification (UQ) and sensitivity analysis (SA) is vital when developing a patient-specific physiological model because it can quantify model output uncertainty and estimate the effect of each of the model's input parameters on the mathematical model. By providing this information, UQ and SA act as diagnostic tools to evaluate model fidelity and compare model characteristics with expert knowledge and real world observation. Computational efficiency is an important part of UQ and SA methods and thus optimization is an active area of research. In this work, we investigate a new efficient sampling method for least-squares polynomial approximation, weighted approximate Fekete points (WAFP). We analyze the performance of this method by demonstrating its utility in stochastic analysis of a cardiovascular model that estimates changes in oxyhemoglobin saturation response. Polynomial chaos (PC) expansion using WAFP produced results similar to the more standard Monte Carlo in quantifying uncertainty and identifying the most influential model inputs (including input interactions) when modeling oxyhemoglobin saturation, PC expansion using WAFP was far more efficient. These findings show the usefulness of using WAFP based PC expansion to quantify uncertainty and analyze sensitivity of a oxyhemoglobin dissociation response model. Applying these techniques could help analyze the fidelity of other relevant models in preparation for clinical application.