Automation of variance reduction methods for solving the transport equation.

Summary Monte-Carlo methods are often used to solve neutron problems. The large size of the problem and the complexity of real geometries make traditional numerical methods difficult to implement. These methods are relatively easy to implement but have the drawback of converging slowly, the accuracy of the calculation being in 1/square root n where n is the number of simulations. Many studies have been conducted to accelerate the convergence of this type of algorithm. This work is part of this trend and aims to research and describe convergence acceleration techniques that can be easily implemented and automated. In this thesis, we are interested in preferential sampling methods. These classical techniques for transport equations use parameters that are usually fixed ernpirically by specialists. The main originality of our work is to propose methods that are easily automated. The originality of the algorithm lies on the one hand in the use of a preferential sampling on the angular variable (angular bias), used in addition to the sampling of the position variable, and on the other hand in the description of an explicit computation technique of all the parameters useful in the variance reduction. This last point allows the almost complete automation of the variance reduction procedure.