Zobrazeno 1 - 10 of 23 pro vyhledávání: '"Mike Pereira"'
3
Geostatistics for large datasets on Riemannian manifolds: a matrix-free approach
Autor: 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 spaces (such as the planet Earth) and they often present nonstationary anis
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e151b134764a9b34f3505da361ace538
http://arxiv.org/abs/2208.12501
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5
Short-term traffic prediction using physics-aware neural networks
Autor: Mike Pereira, Annika Lang, Balázs Kulcsár
Publikováno v: Transportation Research Part C: Emerging Technologies. 142:103772
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 on a physics-aware recurrent neural network. A discretization of a macr
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b47da5239ef14c470d222e8fe8f0607a
https://doi.org/10.1016/j.trc.2022.103772
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6
Parameter and density estimation from real-world traffic data: A kinetic compartmental approach
Autor: Mike Pereira, Pinar Boyraz Baykas, Balázs Kulcsár, Annika Lang
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 improve it. A formulation for a discrete dynamical system is proposed
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a393f5c5483b9a294fcacd3db90c019b
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8
Champs aléatoires généralisés définis sur des variétés riemanniennes : théorie et pratique
Autor: Mike Pereira
Publikováno v: Signal and Image processing. Université Paris sciences et lettres, 2019. English. ⟨NNT : 2019PSLEM055⟩
HAL
Geostatistics is the branch of statistics attached to model spatial phenomena through probabilistic models. In particular, the spatial phenomenon is described by a (generally Gaussian) random field, and the observed data are considered as resulting f
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::0a6d7a5b0ecfd19c21fd68500a3e1b21
https://pastel.archives-ouvertes.fr/tel-02499376/document
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9
Efficient simulation of Gaussian Markov random fields by Chebyshev polynomial approximation
Autor: Nicolas Desassis, Mike Pereira
Publikováno v: Spatial Statistics
Spatial Statistics, Elsevier, 2019, 31, pp.100359. ⟨10.1016/j.spasta.2019.100359⟩
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 when simulating Gaussian Markov random fields obtained by the discret
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1af053fee37bf89f838f2515cc9c62a4
https://hal-mines-paristech.archives-ouvertes.fr/hal-02075386
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