Robust system identification and model predictions in the presence of systematic uncertainty

Context: Model-based data-interpretation techniques are increasingly used to improve the knowledge of complex system behavior. Physics-based models that are identified using measurement data are generally used for extrapolation to predict system behavior under other actions. In order to obtain accurate and reliable extrapolations, model-parameter identification needs to be robust in terms of variations of systematic modeling uncertainty introduced when modeling complex systems. Approaches such as Bayesian inference are widely used for system identification. More recently, error-domain model falsification (EDMF) has been shown to be useful for situations where little information is available to define the probability density function (PDF) of modeling errors. Model falsification is a discrete population methodology that is particularly suited to knowledge intensive tasks in open worlds, where uncertainty cannot be precisely defined. Objective: This paper compares conventional us...

• Type: Journal
• Year: 2015
• Where: AEI
• Authors: Romain Pasquier, Ian F. C. Smith
• Keywords: AEI 2015, Artificial Intelligence