Uncertainty Quantification in Scientific Computing and Engineering
Uncertainty Quantification in Scientific Computing and Engineering

PLATON is an Inria project-team joint with École Polytechnique, within CMAP (Centre de Mathématiques Appliquées, École Polytechnique - UMR7641 CNRS - Institut Polytechnique de Paris), and affiliated with the Inria Saclay Île-de-France Research Center on the École Polytechnique campus.

The global objective of the research proposed within PLATON is to develop advanced numerical methods and practices in simulations, integrating as much as possible the Uncertainty Management. Here, uncertainty management encompasses multiple uncertainty tasks: a) uncertainty characterization (the construction and identification of uncertainty models), b) uncertainty propagation (computation of the model-based prediction uncertainty), c) uncertainty reduction (by inference, data assimilation, conception of new experiments either physical or numerical,. . . ) and d) uncertainty treatment in decision-making processes (sensitivity analysis, risk management, robust optimization,. . . ). Our vision and experience value a strong interaction between all these tasks which, ideally, must be visited in an order commanded by the initial information, the progress of the analysis and the resources available.

Progressing on all these tasks constitutes a significant challenge as the tasks involve a diversity of thematics and skills. This difficulty is prominent in the context of large scale simulations, where practitioners and researchers tend to be highly specialized in specific aspects (modeling, numerical schemes, parallel computing,...). Further, more massive simulations are often confused with better prediction and they overshadow the importance of uncertainties. We believe that using complex models and exploiting fairly the predictions of large-scale simulations need suitable uncertainty management procedures.

We are convinced of the importance of a research effort encompassing as much as possible all uncertainty tasks, to ensure the coherence and mutual relevance of the methods developed. Such an effort focusing on uncertainty management, rather than on a particular application, will be critical to improving the predictive capabilities of simulation tools and address industrial and societal needs. Therefore, the main objectives of the team will be:

  • Propose new methods and approaches for uncertainty management.
  • Develop these methods into numerical tools applicable to large scale simulations.
  • Apply and demonstrate the impact of uncertainty management in real applications with industrial and academic partners.
Centre(s) inria
Inria Saclay Centre
In partnership with


Team leader

Anna Dib

Team assistant