Logical Inference Framework for Experimental Design of Mechanical Characterization Procedures Rus Carlborg, Guillermo Melchor, Juan Inverse problem Inference Bayesian updating Model-class selection Stochastic Inverse problem Probability logic Experimental design 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. 2019-04-03T12:14:27Z 2019-04-03T12:14:27Z 2018-09-07 journal article Logical Inference Framework for Experimental Design of Mechanical Characterization Procedures. Sensors 2018, 18, 2984. 1424-8220 http://hdl.handle.net/10481/55333 eng http://creativecommons.org/licenses/by/3.0/es/ open access Atribución 3.0 España MDPI