Transvalor (France)

output

20222024

most citedPhysics-Informed Graph-Mesh Networks for PDEs: A hybrid approach for complex problems

7 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

J. Alves3 profiles4 · h 9
F. Magoulès2 profiles2 · h 33
M. Chenaud2 profiles2 · h 1
A. Arnoud1 · h 2
A. Markus1 · h 5
Aurélien Pion1 · h 2
Emmanuel Vazquez1 · h 2
Francesca Bugiotti1 · h 12
Frédéric Magoulès1
Meduri Venkata Shivaditya1 · h 1
Michele Esposito Marzino1 · h 1
V. Magnin1 · h 1

collaborating institutions

CentraleSupélecFR4 papers
Mathématiques et Informatique pour la Complexité et les Systèmes2 papers
Université Paris-SaclayFR2 papers
DeltaresNL1 paper
Département mathématiques, informatique, sciences de la donnée et technologies du numériqueFR1 paper
Institut d'électronique de microélectronique et de nanotechnologieFR1 paper
International Monetary FundUS1 paper
Laboratoire des signaux et systèmesFR1 paper
Laboratoire Interdisciplinaire de PhysiqueFR1 paper
Laboratoire Interdisciplinaire des Sciences du NumériqueFR1 paper
University of LiègeBE1 paper
University of PecsHU1 paper

Showing 2024 · math.NAShow all

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math.NA2024★ 7 cited

Physics-Informed Graph-Mesh Networks for PDEs: A hybrid approach for complex problems

Marien Chenaud, Frédéric Magoulès, José Alves

The recent rise of deep learning has led to numerous applications, including solving partial differential equations using Physics-Informed Neural Networks. This approach has proven…