34 citations · 70 across the 13 of their papers we have counts for
14 papers
Stochastic generator of trajectories from record data: application to the fluctuations of a glacier's frontal position from a sample of moraines
Megret Maud, Mike Pereira, Nicolas Eckert +2
The record values theory study elements of a time series that exceed all previous observations, which are of particular interest in fields such as sports or climate science. In thi…
Prediction of spatio-temporal data on meshed surfaces using advection-diffusion SPDEs
Mike Pereira, Lucia Clarotto, Nicolas Desassis
The aim of this work is to propose a statistical model for spatio-temporal data on meshed surfaces based on the Stochastic Partial Differential Equation (SPDE) modeling approach. S…
Uncertainty Quantification of Spline Predictors on Compact Riemannian Manifolds
Charlie Sire, Mike Pereira
To predict smooth physical phenomena from observations, spline interpolation provides an interpretable framework by minimizing an energy functional associated with the Laplacian op…
Spline Interpolation on Compact Riemannian Manifolds
Charlie Sire, Mike Pereira, Thomas Romary
Spline interpolation is a widely used class of methods for solving interpolation problems by constructing smooth interpolants that minimize a regularized energy functional involvin…
Predictive posterior sampling from non-stationnary Gaussian process priors via Diffusion models with application to climate data
Gabriel V Cardoso, Mike Pereira
Bayesian models based on Gaussian processes (GPs) offer a flexible framework to predict spatially distributed variables with uncertainty. But the use of nonstationary priors, often…
Non-stationary Gaussian random fields on hypersurfaces: Sampling and strong error analysis
Erik Jansson, Annika Lang, Mike Pereira
A flexible model for non-stationary Gaussian random fields on hypersurfaces is introduced. The class of random fields on curves and surfaces is characterized by an amplitude spectr…
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…
Short-term traffic prediction using physics-aware neural networks
Mike Pereira, Annika Lang, Balázs Kulcsár
In this work, we propose an algorithm performing short-term predictions of the flux of vehicles on a stretch of road, using past measurements of the flux. This algorithm is based o…
Galerkin--Chebyshev approximation of Gaussian random fields on compact Riemannian manifolds
Annika Lang, Mike Pereira
A new numerical approximation method for a class of Gaussian random fields on compact connected oriented Riemannian manifolds is introduced. This class of random fields is characte…
Parameter and density estimation from real-world traffic data: A kinetic compartmental approach
Mike Pereira, Pinar Boyraz Baykas, Balázs Kulcsár +1
The main motivation of this work is to assess the validity of a LWR traffic flow model to model measurements obtained from trajectory data, and propose extensions of this model to…
The Traffic Reaction Model: A kinetic compartmental approach to road traffic modeling
M. Pereira, B. Kulcsár, Gy. Lipták +2
In this work, a family of finite volume discretization schemes for LWR-type first order traffic flow models (with possible on- and off-ramps) is proposed: the Traffic Reaction Mode…
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 (…
Efficient simulation of Gaussian Markov random fields by Chebyshev polynomial approximation
Mike Pereira, Nicolas Desassis
This paper presents an algorithm to simulate Gaussian random vectors whose precision matrix can be expressed as a polynomial of a sparse matrix. This situation arises in particular…