IMdR “Uncertainty and Industry” workshops

The Institute for risk management (IMdR) is an association helping industrials and public institution to engage in a process of risk evaluation, modelling and mitigation. The “Uncertainty and Industry”) working group focus on probabilistic modelling and the interplay between computer experiment and data science.

Occasional participation is free, register by email at

If you are interested in the group activity, please consider officially joining it.

Computer experiment with dynamical models (I)

25 February 2021

  • Machine Learning for Risk Ranking Automation in IRSN Level 2 PSA

    Guillaume Kioseyian and Marine Marcilhac (IRSN)

    Level 2 PSA producing more and more output data, IRSN has been et developing computing tools since 2017 to perform effective post-treatments and to provide in-depth analysis. These tools have allowed the automation of numerous steps in the post-treatment of release categories generated by the accident progression event tree. The L2 PSA post-treatment is a crucial part to determine and analyse the risk ranking, and consequently to identify critical severe accident scenarios. The analysis of these specific accidental sequences points out the safety improvements to bring about, such as addition or modification of procedures or safety devices. The toolbox developed has been recently completed with a Machine Learning algorithm-based-tool, using Regression Trees method. Thus, for future L2 PSA, a fully automated post-treatment of release categories is available, and the Farmer diagram can be automatically generated for each risk metric. The results obtained with this new method are very satisfactory since the risk ranking automatically obtained is similar to that obtained manually. Moreover, the calculation time for this automatic grouping is about fifteen minutes whereas it is about eight days when done manually. This application paves the way for other automations or process improvements of L2 PSA with the use of Machine Learning approaches

    Keywords: Machine Learning, L2 PSA, risk ranking, severe accident, radiological consequences.

  • Une nouvelle méthode de détection d’outliers fonctionnels pour l’analyse de transitoires accidentels”, Álvaro Rollón De Pinedo, EDF R&D

Computer experiment with dynamical models (II)

25 February 2021

  • Application de la décomposition de Karhunen-Loeve à l’étude simulations thermo-hydrauliques”, Roman Sueur, EDF R&D

  • Réduction de dimension d’ensembles de fonctions par modèles auto-associatifs”, Maéva Caillat and Sylvain Girard Phimeca