Releasing new version
Official release of the latest Uranie version, v4.9.0
We're glad to announce that the latest version, v4.9.0, has been officialy released !
The release notes can be found in the dedicated page here.
Date : 06/08/2024
Open Source
The platform is published with a LGPL license and is available in the download part here. Don't hesiate and give it a try !
Reactive team
The Uranie team is always available for questions ! Do not hesitate to contact the Uranie support here or by mail.
Python compatible
Even though coded in C++, Uranie can be seen as a set of Python libraries and can even be installed through Anaconda.
Easy code interface
Uranie can use your code as a black box and even chain several codes along with analytical functions defined on the spot.
Probabilistic approach
Methodological aspect Python code C++ code
Uranie is built using the probabilistic paradigm for modelling uncertainty. There is a large library of laws for modelling phenomena, as well as different ways of coupling laws and methods for basic calculations (mean, median, standard deviation, quantile, etc.).
Experimental plan
Methodological aspect Python code C++ code
Uranie offers a number of solutions for generating experimental designs, depending on the issues (analysis goals) envisaged, the methodology chosen to achieve them, the size of the input space, the authorised calculation budget, etc.
Launch calculation
Whether the model is an interactive, compiled function or complex code (or even a chain of codes), Uranie can be interfaced to control the launch of calculations in a sequential or distributed way, locally on a machine or on a cluster.
Metamodels
Methodological aspect Python code C++ code
If the code is very resource-intensive, Uranie offers several substitution techniques to generate a model that can replace the code as efficiently as possible to speed up the analyses envisaged.
Sensibility
Methodological aspect Python code C++ code
Uranie offers various techniques for prioritising the inputs to a problem, either qualitatively or quantitatively, depending on the methods used and the calculation budget envisaged, and according to their impact on a defined quantity of interest.
Calibration
Methodological aspect Python code C++ code
Uranie also offers several solutions for calibrating the "magic numbers" of physical models, using a defined data set with or without uncertainty, assuming that the model is verified upstream.
Optimisation
Methodological aspect Python code C++ code
Uranie offers several ways of looking at numerical optimisation, whether single or multi-criteria, with or without constraints, using codes alone or coupling with substitution models in a hybrid approach.
Reliability
Uranie offers a number of algorithms for estimating rare events, often considered as probabilities of failure (hence the name reliability).