Wave-scattering processes: path-integrals designed for the numerical handling of complex geometries.

Relying on Feynman-Kac path-integral methodology, we present a new statistical perspective on wave single-scattering by complex three-dimensional objects. The approach is implemented on three models-Schiff approximation, Born approximation, and rigorous Born series-and familiar interpretative difficulties such as the analysis of moments over scatterer distributions (size, orientation, shape, etc.) are addressed.

Coupling radiative, conductive and convective heat-transfers in a single Monte Carlo algorithm: A general theoretical framework for linear situations.

It was recently shown that radiation, conduction and convection can be combined within a single Monte Carlo algorithm and that such an algorithm immediately benefits from state-of-the-art computer-graphics advances when dealing with complex geometries. The theoretical foundations that make this coupling possible are fully exposed for the first time, supporting the intuitive pictures of continuous thermal paths that run through the different physics at work. First, the theoretical frameworks of propagators and Green's functions are used to demonstrate that a coupled model involving different physical phenomena can be probabilized.

Addressing the gas kinetics Boltzmann equation with branching-path statistics.

This article proposes a statistical numerical method to address gas kinetics problems obeying the Boltzmann equation. This method is inspired by Monte Carlo algorithms used in linear transport physics, where virtual particles are followed backwards in time along their paths. The nonlinear character of gas kinetics translates, in the numerical simulations presented here, into branchings of the virtual particle paths.

Addressing nonlinearities in Monte Carlo.

Monte Carlo is famous for accepting model extensions and model refinements up to infinite dimension. However, this powerful incremental design is based on a premise which has severely limited its application so far: a state-variable can only be recursively defined as a function of underlying state-variables if this function is linear. Here we show that this premise can be alleviated by projecting nonlinearities onto a polynomial basis and increasing the configuration space dimension.