The Uranie platform: an Open-source software for optimisation, meta-modelling and uncertainty analysis

J-B. Blanchard; G. Damblin; J-M. Martinez; G. Arnaud; F. Gaudier

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arXiv · 1803.10656

The Uranie platform: an Open-source software for optimisation, meta-modelling and uncertainty analysis

J-B. Blanchard·G. Damblin·J-M. Martinez·G. Arnaud·F. Gaudier

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Abstract

The high-performance computing resources and the constant improvement of both numerical simulation accuracy and the experimental measurements with which they are confronted, bring a new compulsory step to strengthen the credence given to the simulation results: uncertainty quantification. This can have different meanings, according to the requested goals (rank uncertainty sources, reduce them, estimate precisely a critical threshold or an optimal working point) and it could request mathematical methods with greater or lesser complexity. This paper introduces the Uranie platform, an Open-source framework which is currently developed at the Alternative Energies and Atomic Energy Commission (CEA), in the nuclear energy division, in order to deal with uncertainty propagation, surrogate models, optimisation issues, code calibration... This platform benefits from both its dependencies, but also from personal developments, to offer an efficient data handling model, a C++ and Python interpreter, advanced graphical tools, several parallelisation solutions... These methods are very generic and can then be applied to many kinds of code (as Uranie considers them as black boxes) so to many fields of physics as well. In this paper, the example of thermal exchange between a plate-sheet and a fluid is introduced to show how Uranie can be used to perform a large range of analysis. The code used to produce the figures of this paper can be found in https://sourceforge.net/projects/uranie/ along with the sources of the platform.

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J-B. Blanchard, G. Damblin, J-M. Martinez, G. Arnaud, F. Gaudier. 2018-03-28. The Uranie platform: an Open-source software for optimisation, meta-modelling and uncertainty analysis. https://arxiv.org/abs/1803.10656

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