10 citations · 15 across the 11 of their papers we have counts for
12 papers
A stable deep adversarial learning approach for geological facies generation
Ferdinand Bhavsar, Nicolas Desassis, Fabien Ors +1
The simulation of geological facies in an unobservable volume is essential in various geoscience applications. Given the complexity of the problem, deep generative learning is a pr…
The SPDE approach for spatio-temporal datasets with advection and diffusion
Lucia Clarotto, Denis Allard, Thomas Romary +1
In the task of predicting spatio-temporal fields in environmental science using statistical methods, introducing statistical models inspired by the physics of the underlying phenom…
Geostatistics for large datasets on Riemannian manifolds: a matrix-free approach
Mike Pereira, Nicolas Desassis, Denis Allard
Large or very large spatial (and spatio-temporal) datasets have become common place in many environmental and climate studies. These data are often collected in non-Euclidean space…
A matrix-free approach to geostatistical filtering
Mike Pereira, Nicolas Desassis, Cédric Magneron +1
In this paper, we present a novel approach to geostatistical filtering which tackles two challenges encountered when applying this method to complex spatial datasets: modeling the…
Finite element approximation of non-Markovian random fields
Mike Pereira, Nicolas Desassis
In this paper, we present finite element approximations of a class of Generalized random fields defined over a bounded domain of R d or a smooth d-dimensional Riemannian manifold (…
A general framework for SPDE-based stationary random fields
Ricardo Carrizo Vergara, Denis Allard, Nicolas Desassis
This paper presents theoretical advances in the application of the Stochastic Partial Differential Equation (SPDE) approach in geostatistics. We show a general approach to construc…