The maximum entropy method for data fusion and uncertainty quantification in multifunctional materials and structures
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Oct 08, 2021
- Source ID
- 10.1177/1045389x211048220
Entities
People
- Paul Miles
- Ralph C. Smith
- Wei Gao
- William S Oates
Organizations
- Air Force Office of Scientific Research
- National Science Foundation
- North Carolina State University
- United States Department of Defense
- University of Science and Technology of China