Logical Inference Framework for Experimental Design for Mechanical Characterization Procedures.

Area of Science:

• Engineering
• Physics
• Statistics

Background:

• Experimental design optimization is challenging when indirect reconstruction of physical parameters from sensor data is needed.
• Current methods often involve complex probabilistic models and optimization techniques.

Purpose of the Study:

• To propose a unified probabilistic formulation for reconstructing mechanical parameters and optimizing experimental design.
• To introduce a novel information-theoretic framework with an information density metric.

Main Methods:

• Developed a unified probabilistic formulation for parameter reconstruction and optimization.
• Utilized an information-theoretic framework and a new information density metric.
• Overrode model causality requirements of Bayes' theorem for broader model applicability.

Main Results:

• The proposed formulation offers advantages in extending to concurrent models and new unknowns probabilistically.
• It simplifies reconstruction by avoiding the complex denominator typical in Bayes' theorem.
• Enables solving multiple-model reconstructions and opens avenues for robust reconstruction.

Conclusions:

• The new framework provides a more flexible and simplified approach to experimental design optimization and parameter reconstruction.
• It allows for the integration of multiple models and future extensions, advancing scientific research.