Modeling the Uncertainty in Complex Engineering Systems

TL;DR

This paper introduces a mathematical framework for modeling uncertainty in complex engineering systems by treating system models as learning machines, addressing the limitations of traditional validation methods.

Contribution

It proposes a novel approach that shifts focus from system modeling to uncertainty modeling using computational learning theory, enhancing validation processes.

Findings

Framework effectively captures uncertainty in complex systems

Utilizes learning theory to improve model validation

Provides a new perspective on system behavior analysis

Abstract

Existing procedures for model validation have been deemed inadequate for many engineering systems. The reason of this inadequacy is due to the high degree of complexity of the mechanisms that govern these systems. It is proposed in this paper to shift the attention from modeling the engineering system itself to modeling the uncertainty that underlies its behavior. A mathematical framework for modeling the uncertainty in complex engineering systems is developed. This framework uses the results of computational learning theory. It is based on the premise that a system model is a learning machine.