Multidisciplinary Optimization Competence Center


multidisciplinary optimization

The objective of our competence center is to address four major industrial challenges: shortening the design and development cycle, mastering products for their entire life cycle through the interconnection of systems and digital continuity, ensuring the flexibility and adaptability of the design processes necessary to face market changes, and finally, accelerating the introduction of new technologies in products.

It is, therefore, necessary to develop an ambitious conception and simulation program.



Our activities are thus dedicated to the development of process automation technologies encompassing a wide range of disciplines and parameters, as well as enabling the smooth reconfiguration of these processes. We also fully contribute to digital continuity, building a bridge between Model-Based System Engineering (MBSE) and MDO domains. This allows ensuring the consistency of MDO problem formulations and solutions with the top-level program objectives and system requirements. Finally, our experts focus on developing robust and efficient multidisciplinary optimization methodologies and tools, taking into account uncertainty quantification that can be scaled to industrial applications.

While our scope of application is to design aircraft, generic developments are and can be adapted to other fields – space, automotive, rail, naval, energy – and even more remote applications such as healthcare, climate, and other systems, like means of production.

R&T fields

  • Generic MDO methodologies. MDO formulations (Multidisciplinary Feasible, Individual Discipline Feasible, bi-level formulations family) and algorithms (design of experiment, coupling, optimization), multi-level and multi-fidelity MDO.
  • Methodologies for resolving optimization problems with mixed design variables (continuous and discrete), with applications in the frame of the structure optimization.
  • Machine learning techniques and surrogate modeling.
  • Methodologies for taking into account uncertain parameters in MDO processes.
  • MBSE - MDO methodology is based on system engineering to formalize and grasp the design of complex systems, for example during the global design stage of the aircraft.

GEMSEO – a Python library– is being developed since 2015. It is a scientific software for engineers and researchers used to automatically explore design spaces and find optimal multidisciplinary solutions.

GEMSEO relies on a disruptive approach based on MDO formulations, enabling to generate automatically the MDO process and facilitating its reconfiguration. GEMS proposes interfaces to algorithms libraries (design of experiment, coupling, optimization), surrogate model libraries, post-processing libraries, and visualization capacities.

Since 2021, GEMSEO is open source, under the LGPL v3 license


The generic aspects of GEMSEO allow a wide range of applications. Since 2016, we have been focusing a lot of effort on setting up representative tests of industrial problems and constraints. In particular, a test case for an engine pylon re-design underwent considerable development. Multidisciplinary optimization techniques were applied with success to the re-engining of aircraft to assess the impact of a new generation engine on aircraft performance.

Based on that success, GEMSEO has been used at IRT in multiple applicative domains such as electrical systems optimization, material virtual testing, and heat exchangers design.

Since 2019, GEMSEO has been used in different European projects: H2020 MADELEINE project, Shift2Rail RECET4Rail project, CleanSky2 RHEA project. In particular, the ambition of the latter project is to design future-generation aircraft with ultra-high-aspect-ratio wings using multidisciplinary optimization. In that context, GEMSEO is in charge of orchestrating the overall process, managing different physics-based models through interfaces.

Our offer

  • New MDO methodologies
  • New Generic Engine for MDO Scenarios, Exploration and Optimization (GEMSEO) software (Python library) which enables the automatic creation of MDO processes
  • Machine Learning, uncertainty quantification, and propagation
  • Demonstrations using test cases that simulate industrial constraints
  • Training on MDO and GEMSEO; expertise and support to GEMSEO use Theses reports, scientific publications, communication at conferences

2020 > 2025

FDR optimisation multidisciplinaire
FDR optimisation multidisciplinaire 2

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