Zobrazeno 1 - 10
of 830
pro vyhledávání: '"Cardenas, Juan"'
Autor:
Fuentes, Miguel, Cárdenas, Juan Pablo, Olivares, Gastón, Rasmussen, Eric, Urbina, Carolina, Salazar, Soledad, Vidal, Gerardo
Resilience in social systems is crucial for mitigating the impacts of crises, such as climate change, which poses an existential threat to communities globally. As disasters become more frequent and severe, enhancing community resilience has become i
Externí odkaz:
http://arxiv.org/abs/2411.08015
We introduce a general framework for active learning in regression problems. Our framework extends the standard setup by allowing for general types of data, rather than merely pointwise samples of the target function. This generalization covers many
Externí odkaz:
http://arxiv.org/abs/2306.00945
Autor:
Akter, Mst Shapna, Shahriar, Hossain, Cardenas, Juan Rodriguez, Ahamed, Sheikh Iqbal, Cuzzocrea, Alfredo
One of the most significant challenges in the field of software code auditing is the presence of vulnerabilities in software source code. Every year, more and more software flaws are discovered, either internally in proprietary code or publicly discl
Externí odkaz:
http://arxiv.org/abs/2306.07981
The problem of approximating smooth, multivariate functions from sample points arises in many applications in scientific computing, e.g., in computational Uncertainty Quantification (UQ) for science and engineering. In these applications, the target
Externí odkaz:
http://arxiv.org/abs/2208.12190
Autor:
Figueroa-Cárdenas, Juan de Dios, Canelo Álvarez, Fátima, Pérez, Carlos García, Vargas Campos, Lilia, Hernández Estrada, Zorba Josué, Morales Sánchez, Eduardo
Publikováno v:
In Food Hydrocolloids February 2025 159
Autor:
Cardenas, Juan M., Solano, Manuel
This work analyzes a high order hybridizable discontinuous Galerkin (HDG) method for the linear elasticity problem in a domain not necessarily polyhedral. The domain is approximated by a polyhedral computational domain where the HDG solution can be c
Externí odkaz:
http://arxiv.org/abs/2202.03410
In this chapter, we discuss recent work on learning sparse approximations to high-dimensional functions on data, where the target functions may be scalar-, vector- or even Hilbert space-valued. Our main objective is to study how the sampling strategy
Externí odkaz:
http://arxiv.org/abs/2202.02360
Many problems in computational science and engineering can be described in terms of approximating a smooth function of $d$ variables, defined over an unknown domain of interest $\Omega\subset \mathbb{R}^d$, from sample data. Here both the curse of di
Externí odkaz:
http://arxiv.org/abs/2202.00144
Publikováno v:
Economía, 2023 Jan 01. 22(1), 31-46.
Externí odkaz:
https://www.jstor.org/stable/27302233