Analysis and Uncertainty Quantification of Thermal Transport Measurements through Bayesian Parameter Estimation

TL;DR

This paper advocates for Bayesian parameter estimation as a comprehensive framework for analyzing thermal transport measurements and quantifying uncertainty, demonstrating its advantages over traditional methods through practical examples.

Contribution

It introduces Bayesian parameter estimation to the thermal transport community, providing detailed methodology, code, and comparison with existing techniques for improved analysis and uncertainty quantification.

Findings

Bayesian methods offer interpretable results and error bars.

Incorporating prior knowledge influences inferred parameters.

Comparison shows advantages over traditional analysis techniques.

Abstract

The thermal transport community is increasingly interested in rigorous uncertainty quantification (UQ) of their measurements. In this work, we argue that Bayesian parameter estimation (BPE) represents a powerful framework for both analysis/fitting and UQ. We provide a detailed walkthrough of the technique (including code to duplicate our results) and example analysis based on measuring the thermal conductance of a gold/sapphire interface with FDTR. Comparisons are made against traditional analysis/UQ techniques adopted by the thermal transport community. Notable advantages of BPE include the interpretability of its results, including the capacity to indicate incorrect input assumptions, as well as a way to balance overall goodness of fit against prior knowledge of feasible parameter values. In some cases, incorporating this additional information can affect not only the magnitude of error bars but the inferred values themselves.