Contributions¶
GEMSEO is the result of a collective and ongoing effort bringing together a diverse community of contributors. This page acknowledges the individuals who authored the software, the public and private partners who supported its development, and the research projects that drove its evolution. If you'd like to contribute to the software, please visit this page.
Authors¶
Since 2015, about 40 people have contributed directly to the development and maintenance of GEMSEO. You can find their names below. In addition to them, we would like to thank everyone who has reported bugs, offered suggestions for improvement, shared feedback, o r requested new features—without whom the software would not be what it is today.
Funding¶
GEMSEO is the result of successive projects carried by IRT Saint Exupéry and funded by both the French government (Programme d'Investissements d'Avenir and France 2030) and companies:
These projects have involved both these industrial partners and the following academic partners:
GEMSEO has also benefited from the European research projects
funded by the European Union in the frame of research programmes:
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Projects¶
Key project¶
In 2015, IRT Saint Exupéry launched the MDA-MDO project (2015-2019), with the contributions of the following members: Airbus, Altran, Capgemini, Cerfacs, ISAE-SUPAERO & ICA, and in collaboration with ONERA. This team developed the core elements of GEMSEO by introducing a new paradigm merging dataflow and workflow strategies in order to make design process automation and reconfiguration possible. They added various algorithms (MDA, design of experiments, optimization, MDO formulations, etc.) to apply MDO on several test cases, from state-of-the art to industry-oriented ones.
Maturation¶
IRT Saint Exupéry has brought GEMSEO in several projects to robustify its methods and extend its capabilities.
The MADELEINE project (2017-2021), funded by the European Union's Horizon 2020 research and innovation program under grant agreement No 769025, has improved the scalable data-driven modelling and has developed parallelism capabilities.
The VITAL project (2019-2021) carried by IRT Saint Exupéry, with the contributions of the following members: Airbus and STELIA, contributes to GEMSEO by creating a package for uncertainty quantification and adaptive learning capabilities.
The R-EVOL project (2020-2024) carried by IRT Saint Exupéry implements a machine learning package for surrogate modelling, develops a framework for robust and reliable MDO under uncertainty and makes MDO techniques more efficient thanks to advanced numerical techniques and surrogate-based algorithms. with the contributions of following members: Airbus, Expleo, Altran, Capgemini, Cerfacs, Cenaero and INSA Toulouse.