Dear Colleagues,

Model-based design of experiments (MBDoE) techniques are an effective tool to optimally design a set of experiments when the purpose is to develop reliable, predictive models of a process in the quickest, safest and most efficient way. The goal of MBDoE is to identify the set of model equations describing the system (design for model discrimination) and/or to precisely estimate the set of model parameters (design for improving parameter precision). The effectiveness of MBDoE techniques has been demonstrated in numerous applications in industry and academia and in a wide range of research fields, spanning from system biology to energy systems, to food and reaction engineering, to automation and control.

Although there are many exciting developments in this research area, there are also several challenges which are still unresolved. First of all, MBDoE techniques rely on the prediction of experimental information using the model itself. Factors such as model mismatch, model sloppiness or the presence of disturbances acting on the experimental system can severely affect the identification procedure and lead to the execution of suboptimal or even unfeasible experiments. Secondly, MBDoE applications are often applied to systems with a limited number of parameters or variables, and this limits the applicability to large systems of industrial interest. Finally, in the automation area, online model identification algorithms have been proposed in literature, but their practical applicability is still limited.    

This Special Issue will highlight novel research in the field of optimal experimental design for model identification investigating methodological and/or practical aspects related to MBDoE. Recent advances in MBDoE under model uncertainty and/or novel developments of MBDoE techniques in the context of automation and control engineering would be of great interest in this issue. Furthermore, we particularly welcome studies investigating unconventional and new applications of MBDoE across sectors.

Dr. Federico Galvanin
Dr. René Schenkendorf
Guest Editors

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• model-based design of experiments
• model identification
• optimal experimental design
• model discrimination
• parameter estimation
• robust experimental design
• experimental design under uncertainty
• online design of experiments
• adaptive experimental design
• optimal input design