2022 journal article
The maximum entropy method for data fusion and uncertainty quantification in multifunctional materials and structures
Journal of Intelligent Material Systems and Structures, 10, 1045389X2110482.
author keywords: Maximum entropy; data fusion; uncertainty quantification; dielectric elastomer membrane; ferroelectric domain wall
TL;DR:
The Maximum Entropy (ME) method is applied to more complicated ferroelectric single crystals containing domain structures and soft electrostrictive membranes under both mechanical and electrical loading and finds that parameters, which were initially unidentifiable using a single quantity of interest, become identifiable using multiple quantities of interest.
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Added: October 10, 2021
The quantification of uncertainty in intelligent material systems and structures requires methods to objectively compare complex models to measurements, where the majority of cases include multiple model outputs and quantities of interests given multiphysics coupling. This creates questions about constructing appropriate measures of uncertainty during fusion of data and comparisons between data and models. Novel materials with complex or poorly understood coupling can benefit from advanced statistical analysis to judge models in light of multiphysics data. Here, we apply the Maximum Entropy (ME) method to more complicated ferroelectric single crystals containing domain structures and soft electrostrictive membranes under both mechanical and electrical loading. Multiple quantities of interest are considered, which requires fusing heterogeneous information together when quantifying the uncertainty of lower fidelity models. We find that parameters, which were initially unidentifiable using a single quantity of interest, become identifiable using multiple quantities of interest. We also show that posterior densities may broaden or narrow when multiple data sets are fused together. This is likely due to conflict or agreement, respectively, between the different quantities of interest and the multiple model outputs. Such information is important to advance our predictions of intelligent materials and structures from multi-model inputs and heterogeneous data.