Data-driven Bayesian Deconvolution of Continuous Distributions of Relaxation Times
DOI:
https://doi.org/10.70567/mc.v42.ocsid8313Palavras-chave:
Bayesian deconvolution, Data-driven, Mechanical relaxation spectrum, Linear viscoelasticity, Small amplitude oscillatory shear, UncertaintiesResumo
The knowledge of mechanical properties of materials is based on a precise analysis of their relaxation spectra. The development of methods to deconvolve spectra from measured data, and the assessment of their reliability, is therefore of paramount importance. We present a novel Bayesian deconvolution method based on a physically grounded parameterization of the spectra. We use a Metropolis-Hastings Markov-chain Monte Carlo fitting algorithm, with a full posterior analysis to obtain the best-fitting spectrum and its uncertainties. We test its performance on simulated data, finding that it is unbiased, reliable, and gives precise results even under strong noise.
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