I am a PhD student in statistics at CEA (French Atomic Energy Commission) and in the Applied Mathematics Laboratory (CMAP) of École polytechnique since October 2021. I work under the joint supervision of Josselin Garnier (École polytechnique) and Philippe Humbert (CEA).

• My resume

My research currently focuses on inverse uncertainty quantification methods applied to stochastic neutronics for the non-destructive assay of fissile materials. The methods investigated involve supervised learning techniques like Gaussian process for the surrogate modeling of ressource intensive computer codes.

• Multi-output Gaussian processes for Inverse Uncertainty Quantification in Neutron Noise Analysis

• Sequential design for surrogate modeling in Bayesian inverse problems

• Bayesian Inverse Problem and Uncertainty Quantification in the Joint Analysis of Neutron and Gamma Corrrelations (PHYSOR 2024)

• Uncertainty Quantification in Neutron Noise Analysis using Monte-Carlo Markov Chain methods: an Application to Nuclear Waste Drum Assay (PHYSOR 2022)

• Sensitivity Analysis and Uncertainty Quantification of FFTF Cycle 8C Using the NEAMS Workbench (ANS Winter Meeting 2019)

• Uncertainty quantification for inverse problems in random neutronics using supervised learning and Monte-Carlo Markov chain methods (MASCOT-NUM 2022)

• Status of the NEAMS and ARC neutronic fast reactor tools integration to the NEAMS Workbench

Contact

• Email

  paul.lartaud@polytechnique.edu