Zobrazeno 1 - 10
of 329
pro vyhledávání: '"Cardenas, Luis"'
Autor:
Cardenas, Luis, Zapata, Gianpierre
Publikováno v:
Advances in Intelligent Systems and Computing.
El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.
In Peru, under the framework of the National Policy for the Modernization of public management
In Peru, under the framework of the National Policy for the Modernization of public management
Externí odkaz:
http://hdl.handle.net/10757/656023
Autor:
Quintanilla-Anicama, Mario, Congona-Garcia, Johana, Carvallo-Munar, Edgardo, Macassi-Jauregui, Iliana, Cardenas, Luis
Publikováno v:
Universidad Peruana de Ciencias Aplicadas (UPC)Repositorio Académico - UPCAdvances in Intelligent Systems and Computing.
In the bakery industry, it is sought to have zero defective products. It is in the packing; where it is evident, as the last area of the production chain, all the defects generated. Therefore, a combined redesign method is proposed, which consists in
Externí odkaz:
http://hdl.handle.net/10757/656013
Autor:
Cardenas, Luis, Zapata, Gianpierre
Publikováno v:
Advances in Intelligent Systems and Computing.
El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.
In the Peruvian market there is a great demand for the improvement of the service and quality
In the Peruvian market there is a great demand for the improvement of the service and quality
Externí odkaz:
http://hdl.handle.net/10757/656033
Machine learning algorithms for three-dimensional mean-curvature computation in the level-set method
We propose a data-driven mean-curvature solver for the level-set method. This work is the natural extension to $\mathbb{R}^3$ of our two-dimensional strategy in [DOI: 10.1007/s10915-022-01952-2][1] and the hybrid inference system of [DOI: 10.1016/j.j
Externí odkaz:
http://arxiv.org/abs/2208.09047
Publikováno v:
J. Sci. Comput., 93(1):6, October 2022
We present an error-neural-modeling-based strategy for approximating two-dimensional curvature in the level-set method. Our main contribution is a redesigned hybrid solver [Larios-C\'ardenas and Gibou, J. Comput. Phys. (May 2022), 10.1016/j.jcp.2022.
Externí odkaz:
http://arxiv.org/abs/2201.12342
Publikováno v:
Physical Chemistry Chemical Physics, Royal Society of Chemistry, 2018, 20, pp.25629-25637
An accurate experimental determination of electronic structure in semi-conductors nanopowders is a challenging task. We propose here to combine UPS and UV-Vis spectroscopies in order to get the full description of electronic bands alignment of powder
Externí odkaz:
http://arxiv.org/abs/2201.05369
Publikováno v:
J. Comput. Phys., 471:111623, December 2022
We present a machine learning framework that blends image super-resolution technologies with passive, scalar transport in the level-set method. Here, we investigate whether we can compute on-the-fly, data-driven corrections to minimize numerical visc
Externí odkaz:
http://arxiv.org/abs/2110.11611
Autor:
Molinet-Chinaglia, Clément, Cardenas, Luis, Vernoux, Philippe, Piccolo, Laurent, Loridant, Stéphane
Publikováno v:
In Materials Today Catalysis March 2024 4
Publikováno v:
J. Comput. Phys., 463:111291, August 2022
We present a novel hybrid strategy based on machine learning to improve curvature estimation in the level-set method. The proposed inference system couples enhanced neural networks with standard numerical schemes to compute curvature more accurately.
Externí odkaz:
http://arxiv.org/abs/2104.02951
Publikováno v:
SIAM J. Sci. Comput., 43(3):A1754-A1779, January 2021
We propose a deep learning strategy to estimate the mean curvature of two-dimensional implicit interfaces in the level-set method. Our approach is based on fitting feed-forward neural networks to synthetic data sets constructed from circular interfac
Externí odkaz:
http://arxiv.org/abs/2002.02804