A research frontier has emerged in scientific computation, wherein discretisation error is
regarded as a source of epistemic uncertainty that can be modelled. This raises several …

The worst case integration error in reproducing kernel Hilbert spaces of standard Monte
Carlo methods with n random points decays as n^-1/2 n-1/2. However, the re-weighting of …

In the last decades, different numerical methods have been applied to simulate radiative
transfer in urban configurations. In cases where atmospheric air is treated as an absorbing …

Bayesian cubature provides a flexible framework for numerical integration, in which a priori
knowledge on the integrand can be encoded and exploited. This additional flexibility …

Efficient approximation of incident radiance functions from a set of samples is still an open
problem in physically based rendering. Indeed, most of the computing power required to …

We study and characterize local density fluctuations of ordered and disordered hyperuniform
point distributions on spherical surfaces. In spite of the extensive literature on disordered …

Probabilistic numerical methods aim to model numerical error as a source of epistemic
uncertainty that is subject to probabilistic analysis and reasoning, enabling the principled …

Urbanisation has been a growing trend for several decades. With the rapidly increasing
population, urban areas expand significantly. At the same time, problems related to a higher …

Efficient approximation of incident radiance functions from a set of samples is still an open
problem in physically based rendering. Indeed, most of the computing power required to …

Bayesian Monte Carlo (BMC) is a promising integration technique which considerably
broadens the theoretical tools that can be used to maximize and exploit the information …