Hybrid data regression modelling in measurement

TL;DR

This paper introduces hybrid regression modeling techniques that combine computer simulations and physical experiments to improve measurement accuracy and model quality in experimental systems.

Contribution

It presents novel data integration methods for regression modeling that enhance model completeness, parsimony, and precision in measurement science.

Findings

Hybrid models achieve minimal discrepancy from empirical data.

The approach improves model accuracy and robustness.

Validated on pneumatic gauge measurement data.

Abstract

Measurement involves the determination of quantitative estimates of physical quantities from experiment, along with estimates of their associated uncertainties. Herewith an experimental system model is the key to extracting information from the experimental data. The measurement information obtained depends directly on the quality of the model. With this concern novel regression modelling techniques have been fashioned by data integration from computer-simulation and physical designed experiments. These techniques have allowed attaining the advanced level of model completeness, parsimony, and precision via approximation of the exact unknown model by mathematical product of available theoretical and appropriate empirical functions. The purpose of this approximation is to represent adequately the true model on the considered region of factor space with all advantages of theoretical modelling. This allows a further focus on the measurement science of issue. Pneumatic gauge hybrid data candidate model building, solving and validation reviled that such adequate models permit to attain minimum discrepancy from empirical evidence.