Research > Applied Mathematics, Statistical Data processing, Inverse Problems
This program covers several research projects:
modeling of germination-growth process : this project is carried out in collaboration with the SPIN Division and MINES Nancy and involves several industrial partners (Pechiney, Comhurex, CEA).
It addresses probabilistic and deterministic modeling of germination-growth reactions in non-isobaric, non-isothermal conditions, reduction of variance by Monte-Carlo methods (reverse calculations and uncertainties) and the elaboration of a heterogeneous kinetics computation software for laboratories (in collaboration with the data processing development unit). post-processing for a neutron criticality code
This project is carried out in collaboration with the IRSN Criticality Studies Service in Fontenay aux Roses, France. Its main objective is to use statistical techniques to improve estimates of the effective neutron multiplication coefficient or KEFF, the physical coefficient which measures the degree of criticality of a given neutron configuration. automatic analysis of electrophoresis on proteins
This project is run with the BioMérieux Institute, Saint-Etienne Teaching Hospital, and the School's Signal, Image and Pattern unit. The concept aims at applying image analysis followed by statistical techniques to photographs of protein migrations on gel plates (electrophoresis). This process is used to differentiate the proteins which are characteristic of the presence of cancer. evaluation of weather derivatives
weather derivatives are insurance products which provide compensation for loss or lack of earnings due to climatic risks. Research currently addresses the evaluation of the estimation error in the temperature model developed and the study of stocks of companies with climate-sensitive businesses. digitized experimental designs for petroleum production
This project is run in collaboration with Total and concerns production prediction and calculations of uncertainties, on the basis of incomplete knowledge about the content of oil fields, a history of production and results obtained by a petroleum production simulator. Design of computer experiments are developed to optimize/replace the use of the simulator by response surfaces.