OpenCossan: An Efficient Open Tool for Dealing with Epistemic and Aleatory Uncertainties

In this work, an integrated computational framework for uncertainty quantification and management is presented. Dealing with uncertainty might lead to impractical computational costs especially for detailed models. Hence, it is of paramount importance the availability of efficient numerical methods in order to reduce the computational costs of non-deterministic analyses by implementing the most efficient algorithms and taking advantages of the high performance computing. The computational framework OpenCossan is able to deal with different representation of uncertainty such as random variables, interval, distributional and free p-boxes. A wide range of engineering and scientific problems can be solved by the proposed computational framework thanks to its modular design. An application to a real case multidisciplinary optimization problem involving aleatory and epistemic variables is presented to show the flexibility and the power and the applicability of the software.