Zobrazeno 1 - 10
of 429
pro vyhledávání: '"Guevara, Jorge"'
Autor:
Szwarcman, Daniela1 (AUTHOR), Guevara, Jorge2 (AUTHOR), Macedo, Maysa M. G.2 (AUTHOR), Zadrozny, Bianca1 (AUTHOR), Watson, Campbell3 (AUTHOR), Rosa, Laura4 (AUTHOR), Oliveira, Dario A. B.1,5 (AUTHOR) dario.oliveira@ibm.com
Publikováno v:
Scientific Reports. 8/4/2024, Vol. 14 Issue 1, p1-13. 13p.
Future climate change scenarios are usually hypothesized using simulations from weather generators. However, there only a few works comparing and evaluating promising deep learning models for weather generation against classical approaches. This stud
Externí odkaz:
http://arxiv.org/abs/2107.08074
Autor:
Zadrozny, Bianca, Watson, Campbell D., Szwarcman, Daniela, Civitarese, Daniel, Oliveira, Dario, Rodrigues, Eduardo, Guevara, Jorge
Extreme weather events have an enormous impact on society and are expected to become more frequent and severe with climate change. In this context, resilience planning becomes crucial for risk mitigation and coping with these extreme events. Machine
Externí odkaz:
http://arxiv.org/abs/2102.04534
This paper introduces the concept of kernels on fuzzy sets as a similarity measure for $[0,1]$-valued functions, a.k.a. \emph{membership functions of fuzzy sets}. We defined the following classes of kernels: the cross product, the intersection, the n
Externí odkaz:
http://arxiv.org/abs/1907.12991
Autor:
Fuentes, Estefania, Venegas, Berenice, Muñoz-Arenas, Guadalupe, Moran, Carolina, Vazquez-Roque, Rubén A., Flores, Gonzalo, Treviño, Samuel, Diaz, Alfonso, Guevara, Jorge
Publikováno v:
In Journal of Chemical Neuroanatomy April 2023 129
Autor:
Guevara, Jorge1 (AUTHOR) jorgegd@br.ibm.com, Garcia, Maria1 (AUTHOR), Avegliano, Priscilla1 (AUTHOR), Lima, Debora1 (AUTHOR), Queiroz, Dilermando1 (AUTHOR), Macedo, Maysa1 (AUTHOR), Tizzei, Leonardo1 (AUTHOR), Szwarcman, Daniela1 (AUTHOR), Zadrozny, Bianca1 (AUTHOR), Watson, Campbell1 (AUTHOR), Jones, Anne1 (AUTHOR)
Publikováno v:
Journal of Advances in Modeling Earth Systems. Nov2023, Vol. 15 Issue 11, p1-20. 20p.
Akademický článek
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Akademický článek
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Autor:
Guevara, Jorge, Kormaksson, Matthias, Zadrozny, Bianca, Lu, Ligang, Tolle, John, Croft, Tyler, Wu, Mingqi, Limbeck, Jan, Hohl, Detlef
In recent work, data-driven sweet spotting technique for shale plays previously explored with vertical wells has been proposed. Here, we extend this technique to multiple formations and formalize a general data-driven workflow to facilitate feature e
Externí odkaz:
http://arxiv.org/abs/1705.06556