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Analysis and uncertainty quantification of thermal transport measurements through Bayesian parameter estimation
The Review of Scientific Instruments
|March 20, 2026
Summary
Bayesian parameter estimation (BPE) offers a robust framework for uncertainty quantification (UQ) in thermal transport measurements. This method enhances data analysis by providing interpretable results and incorporating prior knowledge for more accurate parameter inference.
Area of Science:
- Physics
- Materials Science
- Thermodynamics
Background:
- The thermal transport research field requires rigorous uncertainty quantification (UQ) for experimental measurements.
- Traditional analysis and UQ techniques are widely used but have limitations.
Purpose of the Study:
- To present Bayesian parameter estimation (BPE) as a powerful framework for analysis, fitting, and UQ in thermal transport.
- To provide a detailed walkthrough and code for implementing BPE.
Main Methods:
- Frequency domain thermoreflectance was used to measure the thermal conductance of a gold/sapphire interface.
- Bayesian parameter estimation (BPE) was applied for data analysis and uncertainty quantification.
- BPE results were compared against traditional analysis/UQ techniques.
Main Results:
- BPE provides interpretable results, capable of identifying incorrect input assumptions.
- BPE allows balancing goodness of fit with prior knowledge of parameter values.
- Incorporating prior information via BPE can influence both error bar magnitudes and inferred parameter values.
Conclusions:
- Bayesian parameter estimation (BPE) is a valuable and powerful framework for uncertainty quantification (UQ) in thermal transport measurements.
- BPE offers advantages in result interpretability and the incorporation of prior knowledge, leading to potentially more accurate inferences.
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