Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Ricardo Medel Esquivel"'
Publikováno v:
European Physical Journal C: Particles and Fields, Vol 83, Iss 4, Pp 1-18 (2023)
Abstract This paper introduces a new approach to reconstruct cosmological functions using artificial neural networks based on observational measurements with minimal theoretical and statistical assumptions. By using neural networks, we can generate c
Externí odkaz:
https://doaj.org/article/8703003c1174414c8a08fa7b968d0048
Autor:
Ricardo Medel-Esquivel, Isidro Gómez-Vargas, Alejandro A. Morales Sánchez, Ricardo García-Salcedo, José Alberto Vázquez
Publikováno v:
Universe, Vol 10, Iss 1, p 11 (2023)
Genetic algorithms are a powerful tool in optimization for single and multimodal functions. This paper provides an overview of their fundamentals with some analytical examples. In addition, we explore how they can be used as a parameter estimation to
Externí odkaz:
https://doaj.org/article/ab0aa8f77dcf4725853fa695f7351bd6
Publikováno v:
European Physical Journal
This paper introduces a new approach to reconstruct cosmological functions using artificial neural networks based on observational measurements with minimal theoretical and statistical assumptions. By using neural networks, we can generate computatio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1cfaebb2613d1d10935dfca853d04412
http://arxiv.org/abs/2104.00595
http://arxiv.org/abs/2104.00595
Publikováno v:
The Physics Teacher. 59:480-481
One of the main topics of elementary physics is the idea that every material is composed of “little particles that move around in perpetual motion, attracting each other when they are a little distance apart, but repelling upon being squeezed into
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2fa71fb2a468c6977fcee749e7d24baf
https://doi.org/10.1119/10.0006135
https://doi.org/10.1119/10.0006135
Autor:
Teodoro Rivera Montalvo, Ricardo García-Salcedo, Ricardo Medel Esquivel, J. Alberto Vázquez, Isidro Gómez-Vargas
Publikováno v:
Journal of Physics: Conference Series. 1723:012021
In this work, we propose a complete methodology to identify the parameters of a dynamical system from a data set using genetic algorithms. Considering the search for the model parameters as an inverse problem, we numerically solve the differential eq
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c1354a0ddc7889ea58bba4080907172d
https://doi.org/10.1088/1742-6596/1723/1/012021
https://doi.org/10.1088/1742-6596/1723/1/012021
Publikováno v:
Journal of Physics: Conference Series. 1723:012022
In Bayesian inference, the likelihood functions are evaluated thousands of times. In this paper we explore the use of an Artificial Neural Network to learn how to calculate the likelihood function and thus speed up the Bayesian inference process. We
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2f26089bb69773472b8d521d1ae5ca1c
https://doi.org/10.1088/1742-6596/1723/1/012022
https://doi.org/10.1088/1742-6596/1723/1/012022
Publikováno v:
Journal of Physics: Conference Series. 1221:012038
We investigate the cosmic dynamics of Friedmann-Robertson-Walker universes – flat spatial sections – which are driven by nonlinear electrodynamics (NLED) Lagrangian. We pay special attention to the check of the sign of the square sound speed sinc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b836c92b7868c01825217ba0940752bb
https://doi.org/10.1088/1742-6596/1221/1/012038
https://doi.org/10.1088/1742-6596/1221/1/012038
Publikováno v:
Journal of Physics: Conference Series. 1221:012031
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::118e9b1e4b5e24742d8027f6367efcca
https://doi.org/10.1088/1742-6596/1221/1/012031
https://doi.org/10.1088/1742-6596/1221/1/012031
Publikováno v:
Journal of Physics: Conference Series; Jun2019, Vol. 1221 Issue 1, p1-1, 1p
Publikováno v:
Journal of Physics: Conference Series; Jun2019, Vol. 1221 Issue 1, p1-1, 1p