Spectrally refined unbiased Monte Carlo estimate of the Earth's global radiative cooling.

The Earth's radiative cooling is a key driver of climate. Determining how it is affected by greenhouse gas concentration is a core question in climate-change sciences. Due to the complexity of radiative transfer processes, current practices to estimate this cooling require the development and use of a suite of radiative transfer models whose accuracy diminishes as we move from local, instantaneous estimates to global estimates over the whole globe and over long periods of time (decades). Here, we show that recent advances in nonlinear Monte Carlo methods allow a paradigm shift: a completely unbiased estimate of the Earth's infrared cooling to space can be produced using a single model, integrating the most refined spectroscopic models of molecular gas energy transitions over a global scale and over years, all at a very low computational cost (a few seconds).

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. In terms of the computational contribution, we show that commonly recognized features of the Monte Carlo method with respect to geometric complexity can now be available when solving electromagnetic scattering.

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. Second, they are extended and made operational using the Feynman-Kac theory and stochastic processes. Finally, the theoretical framework is supported by a new proposal for an approximation of coupled Brownian trajectories compatible with the algorithmic design required by ray-tracing acceleration techniques in highly refined geometry.

The "teapot in a city": A paradigm shift in urban climate modeling.

Urban areas are a high-stake target of climate change mitigation and adaptation measures. To understand, predict, and improve the energy performance of cities, the scientific community develops numerical models that describe how they interact with the atmosphere through heat and moisture exchanges at all scales. In this review, we present recent advances that are at the origin of last decade's revolution in computer graphics, and recent breakthroughs in statistical physics that extend well-established path-integral formulations to nonlinear coupled models. We argue that this rare conjunction of scientific advances in mathematics, physics, computer, and engineering sciences opens promising avenues for urban climate modeling and illustrate this with coupled heat transfer simulations in complex urban geometries under complex atmospheric conditions. We highlight the potential of these approaches beyond urban climate modeling for the necessary appropriation of the issues at the heart of the energy transition by societies.

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. Considering phytoplankton growth in light-limited environments, radiative transfer in planetary atmospheres, electromagnetic scattering by particles, and concentrated solar power plant production, we prove the real-world usability of this advance in four test cases which were previously regarded as impracticable using Monte Carlo approaches. We also illustrate an outstanding feature of our method when applied to acute problems with interacting particles: handling rare events is now straightforward. Overall, our extension preserves the features that made the method popular: addressing nonlinearities does not compromise on model refinement or system complexity, and convergence rates remain independent of dimension.