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Bayesian model selection for complex flows of yield stress fluids
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DOI：10.1016/j.jnnfm.2026.105639.png)
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Abstract
En 中文
• Develop a Bayesian framework for model calibration and selection of yield stress fluids. • Quantify model bias and experimental uncertainty explicitly in complex flows. • Demonstrate Occam’s razor in Bayesian selection of rheological models. • Reveal limitations of rheo-informed priors for predicting squeeze flows. • Enable accurate predictions for complex flows using broad, uninformative priors.
Keywords:
Uncertainty quantification
Model selection
Bayesian inference
Soft matter
Rheology
Markov chain Monte Carlo
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2.8
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153
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