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Uncertainty quantification and phenomenological knowledge: Predictive power of emulator models for tail behaviour of safety critical systems


The project will seek to establish the necessary mathematical framework for introducing phenomenological knowledge in response surface models. These are fast running approximations of complex time consuming computer simulations or expensive physical experiments, based on limited realizations of the simulation/experiment and statistical machine learning. This approach is especially relevant for probabilistic analysis of rare events, such as structural failure in safety critical systems, where not enough data exist to rely on observations alone. Some of the key challenges today relate to the rapid increase in structural complexity of engineering systems, together with the need for more accurate and reliable models to support decision making under uncertainty. Here, today's purely statistical approach is not sufficient. By introducing constraints based on phenomenological knowledge we believe that we can overcome these challenges, and develop the mathematical tools needed for probabilistic analysis of complex engineering systems in the digital era. It will thus form a basis for update of existing and development of future rules and recommended practices delivered by DNV GL. The main focus of the project is to develop new mathematical tools. To demonstrate how these tools may be applied, some relevant applications from the Oil & Gas industry will be selected for numerical experimentation.

Project leader: Simen Eldevik

Started: 2017

Ends: 2021

Category: Næringsliv

Sector: Næringsliv

Budget: 1660000

Institution: DNV AS

Address: Bærum