Cossan software : a multidisciplinary and collaborative software for uncertainty quantification
Patelli, Edoardo and Broggi, Matto and Tolo, Silvia and Sadeghi, Jonathan; Stefanou, George and Papadrakakis, M. and Papadopoulos, Vissarion, eds. (2017) Cossan software : a multidisciplinary and collaborative software for uncertainty quantification. In: UNCECOMP 2017. National Technical University of Athens, GRC, pp. 212-224. ISBN 9786188284449 (https://doi.org/10.7712/120217.5364.16982)
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Abstract
Computer-Aided modelling and simulation is now widely recognised as the third 'leg' of scientific method, alongside theory and experimentation. Many phenomena can be studied only by using computational processes such as complex simulations or analysis of experimental data. In addition, in many engineering fields computational approaches and virtual prototypes are used to support and drive the design of new components, structures and systems. A general purpose software for uncertainty quantification and risk assessment, named COSSAN, is under continuous development. This is a multi-disciplinary software that satisfies industry requirements regarding numerical efficiency and analysis of detailed models that can be used to solve a wide range of industrial and scientific problems. The main aim of the COSSAN software is to allow the inclusion of non-deterministic analyses as a practice standard routing in scientific computing. This paper presents two recent toolboxes added to the OPENCOSSAN: Credal Networks and Interval Predictive model.
ORCID iDs
Patelli, Edoardo ORCID: https://orcid.org/0000-0002-5007-7247, Broggi, Matto, Tolo, Silvia and Sadeghi, Jonathan; Stefanou, George, Papadrakakis, M. and Papadopoulos, Vissarion-
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Item type: Book Section ID code: 72210 Dates: DateEvent17 June 2017PublishedSubjects: Social Sciences > Industries. Land use. Labor > Risk Management Department: Faculty of Engineering > Civil and Environmental Engineering Depositing user: Pure Administrator Date deposited: 30 Apr 2020 09:47 Last modified: 19 Sep 2024 00:29 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/72210