Neural network approach to sensitivity and uncertainty analysis

Computer simulation of the dynamic evolution of complex systems has become a fundamental tool for many modern engineering activities. In particular, risk-informed design projects and safety analyses require that the system behavior be analyzed under several diverse conditions in the presence of substantial model and parameter uncertainty which must be accounted for. In this paper we investigate the capabilities of artificial neural networks of providing both a first-order sensitivity measure of the importance of the various parameters of a model and a fast, efficient tool for dynamic simulation, to be used in uncertainty analyses. The dynamic simulation of a steam generator is considered as a test-bed to show the potentialities of these tools and to point out the difficulties and crucial issues which typically arise when attempting to establish an efficient neural network structure for sensitivity and uncertainty analyses.