Mechanistic-statistical concurrent modelling techniques

Abstract

Measurements involve the determination of physical quantities by experiment. In this endeavour, an experimental model will need to specify how measurement system is expected to respond to input data, which is the key to extracting information from the system. The quality of information depends directly on the quality of the model. With this concern novel techniques for model quality improvement have been fashioned. For attaining a high level of comprehensiveness, accuracy and precision, the exact unknown model was approximated simultaneously by available mechanistic and appropriate empirical functions. Adequate modelling was accomplished by employing theoretical and empirical data integration. Herewith, additive and multiplicative approaches were elaborated. The application of developed techniques for sensor model perfection has shown that concurrent multiplicative modelling, in comparison with pure statistical modelling, permits the attainment of less discrepancy in experimental evidence for the whole region of interest for model input variables.