Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Jefferson Fialho"'
Autor:
Breno Orzari, Nadezda Chernyavskaya, Raphael Cobe, Javier Duarte, Jefferson Fialho, Dimitrios Gunopulos, Raghav Kansal, Maurizio Pierini, Thiago Tomei, Mary Touranakou
Publikováno v:
Machine Learning: Science and Technology, Vol 4, Iss 4, p 045023 (2023)
In high energy physics, one of the most important processes for collider data analysis is the comparison of collected and simulated data. Nowadays the state-of-the-art for data generation is in the form of Monte Carlo (MC) generators. However, becaus
Externí odkaz:
https://doaj.org/article/75b47c62b99d4ebaa1d38fe08ae5ec2f
Autor:
Mathias, Marlon Sproesser, de Almeida, Wesley Pereira, de Barros, Marcel Rodrigues, Coelho, Jefferson Fialho, de Freitas, Lucas Palmiro, Moreno, Felipe Marino, Netto, Caio Fabricio Deberaldini, Cozman, Fabio Gagliardi, Costa, Anna Helena Reali, Tannuri, Eduardo Aoun, Gomi, Edson Satoshi, Dottori, Marcelo
Publikováno v:
Intelligent Systems, Cham, 2022, pp. 388-401
We implement a Physics-Informed Neural Network (PINN) for solving the two-dimensional Burgers equations. This type of model can be trained with no previous knowledge of the solution; instead, it relies on evaluating the governing equations of the sys
Externí odkaz:
http://arxiv.org/abs/2301.07824
Autor:
Felipe Leno Da Silva, Silvio Stanzani, Jefferson Fialho, Jorge Mondadori, Muriel Mazzetto, Felipe Sanches Couto, Raphael Cobe
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 35:15640-15646
The industry demand for AI experts raised to unprecedented levels in the last years. However, the increasing demand was not met by the number of skilled professionals in this area. As an effort to mitigate this problem, many companies create AI resid
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6b98ccab0f424d54e4b129159d3b2dfe
https://doi.org/10.1609/aaai.v35i17.17842
https://doi.org/10.1609/aaai.v35i17.17842
Autor:
Júlio Amaral, Rogerio Iope, Silvio Luiz Stanzani, Marco Gomes, Jefferson Fialho, Raphael Cóbe, Artur Baruchi
Publikováno v:
High Performance Computing for Computational Science – VECPAR 2018 ISBN: 9783030159955
VECPAR
VECPAR
Performance prediction of applications has always been a great challenge, even for homogeneous architectures. However, today’s trend is the design of cluster running in a heterogeneous architecture, which increases the complexity of new strategies
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4113e2a99ed1a9acb31e3d2d8b4ac912
https://doi.org/10.1007/978-3-030-15996-2_18
https://doi.org/10.1007/978-3-030-15996-2_18