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 …

Since its initial development as a specialty technique for modeling neutron transport in fissile
materials almost 80 years ago, the Monte Carlo method has since been deployed in almost …

The seventh edition of this classic text outlines the fundamental physical principles of
thermal radiation, as well as analytical and numerical techniques for quantifying radiative …

Although provably robust to translational perturbations, convolutional neural networks
(CNNs) are known to suffer from extreme performance degradation when presented at test …

Interactions between clouds and radiation are at the root of many difficulties in numerically
predicting future weather and climate and in retrieving the state of the atmosphere from …

Modeling the thermal behavior of a photovoltaic system is one step toward a better
simulation of its electrical performances. In this study, a numerical model of the energy …

In the past decades, Monte Carlo methods have shown their ability to solve PDEs,
independently of the dimensionality of the integration domain and for different use-cases (eg …

We introduce a general framework for transforming biased estimators into unbiased and
consistent estimators for the same quantity. We show how several existing unbiased and …

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 …

The estimation of repeatedly nested expectations is a challenging task that arises in many
real-world systems. However, existing methods generally suffer from high computational …