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pro vyhledávání: '"MARTINEZ, JEAN"'
Several Tensor Basis Neural Network (TBNN) frameworks aimed at enhancing turbulence RANS modeling have recently been proposed in the literature as data-driven constitutive models for systems with known invariance properties. However, persistent ambig
Externí odkaz:
http://arxiv.org/abs/2403.11746
Autor:
Daniel, Geoffrey, Yahiaoui, Mohamed Bahi, Comtat, Claude, Jan, Sebastien, Kochebina, Olga, Martinez, Jean-Marc, Sergeyeva, Viktoriya, Sharyy, Viatcheslav, Sung, Chi-Hsun, Yvon, Dominique
Publikováno v:
Engineering Applications of Artificial Intelligence, Volume 131, 2024, 107876
This article presents a physics-informed deep learning method for the quantitative estimation of the spatial coordinates of gamma interactions within a monolithic scintillator, with a focus on Positron Emission Tomography (PET) imaging. A Density Neu
Externí odkaz:
http://arxiv.org/abs/2310.06572
Autor:
Laurent, Olivier, Lafage, Adrien, Tartaglione, Enzo, Daniel, Geoffrey, Martinez, Jean-Marc, Bursuc, Andrei, Franchi, Gianni
Deep Ensembles (DE) are a prominent approach for achieving excellent performance on key metrics such as accuracy, calibration, uncertainty estimation, and out-of-distribution detection. However, hardware limitations of real-world systems constrain to
Externí odkaz:
http://arxiv.org/abs/2210.09184
The Reynolds-Averaged Navier-Stokes (RANS) approach remains a backbone for turbulence modeling due to its high cost-effectiveness. Its accuracy is largely based on a reliable Reynolds stress anisotropy tensor closure model. There has been an amount o
Externí odkaz:
http://arxiv.org/abs/2208.14301
Autor:
Quintarelli, Julia Mançano, Garnier, Jérémie, Rudrigues de Souza, João Pedro, de Sousa Tonhá, Myller, Barral, Uidemar Morais, Roig, Henrique Llacer, Martinez, Jean-Michel, Santini, William, Puita, Oscar, Seyler, Patrick, Kutter, Vinicius, Souza, Jurandir Rodrigues
Publikováno v:
In Journal of South American Earth Sciences 1 October 2024 145
Autor:
Cordeiro, Gabriel Antonio Rodrigues Velloso, Ianniruberto, Marco, Roig, Henrique Llacer, Ferreira, Osmair Santos, Olivetti, Diogo, Alves e Santos, Diego Raphael, Martinez, Jean-Michel
Publikováno v:
In Environmental Challenges August 2024 16
Autor:
Alves e Santos, Diego R., Martinez, Jean-Michel, Olivetti, Diogo, Zumak, André, Guimarães, David, Aniceto, Keila, Severo, Ednaldo, Ferreira, Osmair, Harmel, Tristan, Cordeiro, Mauricio, Fillizola, Naziano, Sell, Bruna, Fernandes, Daniel, Souto, Camila, Roig, Henrique
Publikováno v:
In International Journal of Applied Earth Observation and Geoinformation April 2024 128
In this paper, we address the estimation of the sensitivity indices called "Shapley eects". These sensitivity indices enable to handle dependent input variables. The Shapley eects are generally dicult to estimate, but they are easily computable in th
Externí odkaz:
http://arxiv.org/abs/2006.02087
Pain relief devoid of opioid side effects following central action of a silylated neurotensin analog
Autor:
Tétreault, Pascal, Besserer-Offroy, Élie, Brouillette, Rebecca L., René, Adeline, Murza, Alexandre, Fanelli, Roberto, Kirby, Karyn, Parent, Alexandre J., Dubuc, Isabelle, Beaudet, Nicolas, Côté, Jérôme, Longpré, Jean-Michel, Martinez, Jean, Cavelier, Florine, Sarret, Philippe
Publikováno v:
Eur J Pharmacol, 173174 (2020)
Neurotensin (NT) exerts naloxone-insensitive antinociceptive action through its binding to both NTS1 and NTS2 receptors and NT analogs provide stronger pain relief than morphine on a molecular basis. Here, we examined the analgesic/adverse effect pro
Externí odkaz:
http://arxiv.org/abs/2005.01887
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