Rus, GuillermoMelchor, Juan2023-02-122023-02-122018-09-01http://hdl.handle.net/10668/19326Optimizing 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.enAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/inverse probleminference Bayesian updatingmodel-class selectionstochastic inverse problemprobability logicexperimental designStructural modelsSelectionSimulationresearch articleopen access10.3390/s180929841424-8220https://www.mdpi.com/1424-8220/18/9/2984/pdf446940600243