Scientific Committee

It actively contributes to ESI Group's decision-making for best investment strategy and innovation solutions in all technological sectors.

Composition of the Scientific Committee

The president's vision

For Professor Francisco Chinesta, ESI's PPL (Product Performance Lifecycle) solutions address the complete lifecycle of an industrial product by including the modeling variations of its prospective operational life, from launch and commissioning up to final withdrawal from service, which goes way beyond the traditional PLM focus of the incumbent market offering. ESI is today fully aligned with the strategic and global challenges of the ‘industry of the future’ and of its connected objects and ‘smart’ autonomous products.

Creation of an Academic Chair

Professor Francisco Chinesta insists on the democratization of numerical simulation and the need for faster and cheaper simulation solutions, to build a product’s “virtual twin”. In fact, this objective motivated the creation of an Academic Chair by the “Ecole Centrale de Nantes” and ESI Group in 2013, headed by Professor Chinesta himself. The Chair gathers 30 researchers from Centrale Nantes, who work closely with ESI Group towards the development of innovative technologies and simulation systems, which will later be implemented into ESI’s software solutions.

In particular, they work on parametric solutions using Model Order Reduction techniques to represent a model for any given set of parameters (material parameters, geometrical parameters, initial conditions or/and applied loads). Using the "Proper Generalized Decomposition Method", the virtual charts obtained enable rapid visualization of complex mathematical relationships on computer screen or tablet. They allow real-time simulations, real-time optimization, inverse analysis, or the real-time uncertainty propagation. Such solutions define a sort of computational vademecum guide, which can be combined with other Model Reduction techniques running online, result in advanced simulation applications. These tools are instrumental in democratizing numerical simulation, as well as contributing for speeding-up existing simulation solutions.

Other innovative topics studied by the Chair involve data-based engineering science and technology, where the use of machine learning strategies could probably constitute a new paradigm in simulation-based engineering sciences. In this case, the solution can be obtained in real-time. The intensive use of constitutive relations can be alleviated by connecting directly experimental tests with simulation strategies, while avoiding the consideration of constitutive relations and their calibration procedures, both limited in many cases. This new generation of data-driven simulations could open unimaginable possibilities in all the areas of engineering.

These two ingredients (computational guides & data-driven simulation) should contribute to the development of efficient “digital twins”, that without any doubt, will be the main protagonists in the Industry of the Future...