Dirichlet-Neumann Averaging: The DNA of Efficient Gaussian Process
Simulation
Dirichlet-Neumann Averaging: The DNA of Efficient Gaussian Process
Simulation
Gaussian processes (GPs) and Gaussian random fields (GRFs) are essential for modelling spatially varying stochastic phenomena. Yet, the efficient generation of corresponding realisations on high-resolution grids remains challenging, particularly when a large number of realisations are required. This paper presents two novel contributions. First, we propose a new methodology based …