Two examples are used to illustrate the proposed methodology for model uncertainty characterisation, with insights, discussions, and comparison with previous methods.
In their efforts to model uncertainty, decision theorists have proposed many methodologies with the majority of them having been based on statistics and probability[ 1- 4], information theory and entropy[ 5], or possibilistic approaches such as fuzzy logic[ 6, 7].
In this research, a methodology is proposed to quantify model uncertainty using measured differences between experimental data and model outcomes under a Bayesian statistical framework.
We will argue in this paper that the nature and magnitude of model uncertainty dictate the appropriateness of the control system design methodology.
New methods and methodology may be required to properly model uncertainty.
The objective of this research is to develop control methodologies to reduce the effect of model uncertainty on structural damage detection.
Key steps of the methodology consist of assessing static load effect, dynamic amplification, model uncertainty, sensitivity factors and target reliability.
In this paper we propose a methodology for active failure detection for sampled-data systems in the presence of model uncertainty.
We present a general methodology, history matching, that can be used to investigate the effect of observational and model uncertainty on inverse modelling studies.
This paper presents a methodology to quantify uncertainty in vulnerability assessment for buildings affected by debris flow, focusing on model uncertainty.
