RT Journal Article
T1 Logical Inference Framework for Experimental Design of Mechanical Characterization Procedures
A1 Rus, Guillermo
A1 Melchor, Juan
K1 inverse problem
K1 inference Bayesian updating
K1 model-class selection
K1 stochastic inverse problem
K1 probability logic
K1 experimental design
K1 Structural models
K1 Selection
K1 Simulation
AB Optimizing an experimental design is a complex task when a model is required for indirect reconstruction of physical parameters from the sensor readings. In this work, a formulation is proposed to unify the probabilistic reconstruction of mechanical parameters and an optimization problem. An information-theoretic framework combined with a new metric of information density is formulated providing several comparative advantages: (i) a straightforward way to extend the formulation to incorporate additional concurrent models, as well as new unknowns such as experimental design parameters in a probabilistic way; (ii) the model causality required by Bayes' theorem is overridden, allowing generalization of contingent models; and (iii) a simpler formulation that avoids the characteristic complex denominator of Bayes' theorem when reconstructing model parameters. The first step allows the solving of multiple-model reconstructions. Further extensions could be easily extracted, such as robust model reconstruction, or adding alternative dimensions to the problem to accommodate future needs.
PB Mdpi
YR 2018
FD 2018-09-01
LK http://hdl.handle.net/10668/19326
UL http://hdl.handle.net/10668/19326
LA en
DS RISalud
RD Apr 7, 2025