Data-driven Bayesian Deconvolution of Continuous Distributions of Relaxation Times
DOI:
https://doi.org/10.70567/mc.v42.ocsid8313Palabras clave:
Bayesian deconvolution, Data-driven, Mechanical relaxation spectrum, Linear viscoelasticity, Small amplitude oscillatory shear, UncertaintiesResumen
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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Derechos de autor 2025 Asociación Argentina de Mecánica Computacional
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
Esta publicación es de acceso abierto diamante, sin ningún tipo de costo para los autores ni los lectores.
Solo se publicarán aquellos trabajos que han sido aceptados para su publicación y han sido presentados en el congreso de AMCA.
