Abstract
Abstract The regularization quadratic programming approach to infer relaxation spectra from experimental mechanical data was studied. It was found that the inferior of the sum of the squared difference between the input data and the back-calculated values did not yield a satisfactory relaxation spectrum as the regularization weighting parameter α was increased. A modification was made to the function to be minimized so that all data points were equally weighted. Quadratic programming was then found to be sufficient to infer a reliable relaxation spectrum if the input experimental data had a high degree of accuracy. When the experimental data were not sufficiently accurate, regularization over-regularized the high-value region of the solution spectrum before it could improve the low-value region. Decreasing the number of sought-after points in the solution spectrum can compensate for the noise level in the input experimental data and will allow inferring a reliable relaxation spectrum from the experimental...
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