Zobrazeno 1 - 10
of 840
pro vyhledávání: '"Franck Emmanuel"'
Autor:
Franck Emmanuel, Labanni Ibtissem, Nasseri Youssouf, Navoret Laurent, Parasiliti Rantone Giuseppe, Steimer Guillaume
Publikováno v:
ESAIM: Proceedings and Surveys, Vol 77, Pp 213-228 (2024)
This paper proposes a reduced model to simulate the one-dimensional Vlasov-Poisson equation with the non-linear Fokker-Planck operator. The model provides the space-time dynamics of a few macroscopic quantities constructed following the Reduced Order
Externí odkaz:
https://doaj.org/article/45c93097b597406687c8ad6bb3bcb21a
Autor:
Boujoudar Mohammed, Franck Emmanuel, Hoch Philippe, Lasuen Clément, Le Hénaff Yoan, Paragot Paul
Publikováno v:
ESAIM: Proceedings and Surveys, Vol 77, Pp 123-144 (2024)
In this work we focus on an adaptation of the method described in [1] in order to deal with source term in the 2D Euler equations. This method extends classical 1D solvers (such as VFFC, Roe, Rusanov) to the two-dimensional case on unstructured meshe
Externí odkaz:
https://doaj.org/article/97869546da5445d4aae72547c2994e09
Autor:
Fabio Efficace, Francois-Xavier Mahon, Johan Richter, Alfonso Piciocchi, Marta Cipriani, Franck Emmanuel Nicolini, Henrik Hjorth-Hansen, Antonio Almeida, Jeroen J. W. M. Janssen, Jiri Mayer, Perttu Koskenvesa, Panayiotis Panayiotidis, Ulla Olsson-Strömberg, Joaquín Martinez-Lopez, Philippe Rousselot, Hanne Vestergaard, Hans Ehrencrona, Veli Kairisto, Katerina Machova Polakova, Satu Mustjoki, Marc Berger, Andreas Hochhaus, Markus Pfirrmann, Susanne Saussele
Publikováno v:
HemaSphere, Vol 7, p e0972454 (2023)
Externí odkaz:
https://doaj.org/article/a078e335813f413cb617882efe61628c
Autor:
Markus Pfirrmann, Francois-Xavier Mahon, Stéphanie Dulucq, Andreas Hochhaus, Panayiotis Panayiotidis, Antonio Almeida, Jiri Mayer, Henrik Hjorth-Hansen, Jeroen J. W. M. Janssen, Satu Mustjoki, Joaquín Martinez-Lopez, Hanne Vestergaard, Hans Ehrencrona, Veli Kairisto, Kateřina Machová Poláková, Franck Emmanuel Nicolini, Wolf-Karsten Hofmann, Joëlle Guilhot, Susanne Saussele, Johan Richter
Publikováno v:
HemaSphere, Vol 7, p e230884b (2023)
Externí odkaz:
https://doaj.org/article/f21cdde0c6114e2fb555ae09f10c9dbe
Autor:
Martin C. Müller, Jorge Cortes, Charles Chuah, Daniel Deangelo, Michael Deininger, François Guilhot, Timothy Hughes, Franck Emmanuel Nicolini, Javier Pinilla-Ibarz, Delphine Rea, Gianantonio Rosti, Neil P. Shah, Moshe Talpaz, Vickie Lu, Thihan Padukkavidana, Hagop Kantarjian
Publikováno v:
HemaSphere, Vol 7, p e88427a9 (2023)
Externí odkaz:
https://doaj.org/article/eb3dd58a194242debe7a5d6162af5c8c
Autor:
Franck Emmanuel, Hivert Hélène, Latu Guillaume, Leman Hélène, Maury Bertrand, Mehrenberger Michel, Navoret Laurent
Publikováno v:
ESAIM: Proceedings and Surveys, Vol 77, Pp 1-1 (2024)
Externí odkaz:
https://doaj.org/article/bd0fd64c806d418288de9d66a030f888
Autor:
Stephanie Dulucq, Sandrine Hayette, Jean-Michel Cayuela, Frédéric Bauduer, Kaddour Chabane, Patrice Chevallier, Pascale Cony-Makhoul, Pascale Flandrin-Gresta, Caroline Le Jeune, Yannick Le Bris, Laurence Legros, Hervé Maisonneuve, Lydia Roy, Francois-Xavier Mahon, Ivan Sloma, Delphine Rea, Franck Emmanuel Nicolini
Publikováno v:
Haematologica, Vol 107, Iss 12 (2022)
Externí odkaz:
https://doaj.org/article/948a36bc449e433f8635a69bf0a66dd6
In this work, we explore the numerical solution of geometric shape optimization problems using neural network-based approaches. This involves minimizing a numerical criterion that includes solving a partial differential equation with respect to a dom
Externí odkaz:
http://arxiv.org/abs/2407.19064
Publikováno v:
Agronomy, Vol 13, Iss 4, p 975 (2023)
Weeds cause more crop yield loss and increase farmers’ production costs more than any other agricultural pest worldwide. Natural extracts can be an important alternative to synthetic herbicides, or they can be one of the resources from which to dis
Externí odkaz:
https://doaj.org/article/1f56f45bdfb3405bb13238652854dded
Autor:
Fiorini, Camilla, Flint, Clément, Fostier, Louis, Franck, Emmanuel, Hashemi, Reyhaneh, Michel-Dansac, Victor, Tenachi, Wassim
Symbolic Regression (SR) is a widely studied field of research that aims to infer symbolic expressions from data. A popular approach for SR is the Sparse Identification of Nonlinear Dynamical Systems (\sindy) framework, which uses sparse regression t
Externí odkaz:
http://arxiv.org/abs/2404.15742