A5017875763- Numerical methods in engineering
- Advanced Numerical Methods in Computational Mathematics
- Advanced Numerical Analysis Techniques
- Mathematical Biology Tumor Growth
- 3D Surveying and Cultural Heritage
- Numerical methods for differential equations
- Computer Graphics and Visualization Techniques
- Computational Geometry and Mesh Generation
- Advanced Mathematical Modeling in Engineering
- Differential Equations and Numerical Methods
- Nonlinear Waves and Solitons
- Fractional Differential Equations Solutions
- 3D Modeling in Geospatial Applications
- Electromagnetic Simulation and Numerical Methods
- Image Processing and 3D Reconstruction
- 3D Shape Modeling and Analysis
- Remote Sensing and LiDAR Applications
- Geotechnical Engineering and Underground Structures
- Model Reduction and Neural Networks
- Nonlinear Partial Differential Equations
- Seismic Imaging and Inversion Techniques
- Cancer Cells and Metastasis
- Numerical methods in inverse problems
- Augmented Reality Applications
- Composite Material Mechanics
National University of Distance Education
2016-2024
Universidad de Jaén
2004-2023
Preeclampsia affects from 5% to 14% of all pregnant women and is responsible for about maternal deaths per year in the world. This paper focused on use a decision analysis tool early detection preeclampsia at risk. applies fuzzy linguistic approach implemented wearable device. In order develop this tool, real dataset containing data with high risk health center has been analyzed, methodology two main phases used. Firstly, transformation applied increase interpretability flexibility...
Learning to write computer programs and engaging students in introductory programming subjects are difficult tasks. This study presents the results obtained when using flipped classroom peer instruction methodologies an course of programming. Before lecture, out-of-class on-line homework questionnaires were applied, was used encourage students' participation discussion, assisted by on-the-fly assessment app. The main academic results, degree involvement activities, feedback collected...
We introduce a meshless method derived by considering the time variable as spatial without need to extend further conditions solution of linear and non-linear parabolic PDEs. The is based on moving least squares method, more precisely, generalized finite difference (GFDM), which allows us select well-conditioned stars. Several 2D 3D examples, including variable, are shown for both regular irregular node distributions. results compared with explicit GFDM in terms errors execution time.
We introduce a meshless method derived by considering the time variable as spatial without need to extend further conditions solution of linear and non-linear hyperbolic PDEs. The is based on moving least squares method, more precisely in Generalized Finite Difference Method which allows us select well-conditioned stars. Several 2D 3D examples including are shown for both regular irregular node distributions. results compared with explicit GFDM terms errors execution time.
The generalized finite differences method allows the use of irregular clouds nodes. optimal values key parameters vary depending on how nodes in cloud are distributed, and this can be complicated especially 3D. Therefore, we establish 2 criteria to allow automation selection process parameters. These depend discrete functions, one them penalizes distances other imbalances. In addition, show generate more efficient than finer regular We propose an improved versatile h‐adaptive that adding,...
In this paper we present an application of the Generalized Finite Difference Method to solve generalized nonlinear Benjamin–Bona–Mahony–Burgers equation in 2D and 3D. We provide two approaches solving BBMB equation. First, directly use explicit GFD scheme, second, transform into a PDE system. further attempt find criteria for convergence fully method using GFDM Benjamin–Bona–Mahony–Bourgers
Abstract The digitization of some the processes carried out in an archaeological excavation is changing way working at site. Today, new technologies coexist with traditional methodologies. study stratigraphy can combine drawings profiles and plans, Harris Matrix diagram, as well digitized files that perform a complete record stratigraphic sequence. However, this information usually unaggregated from rest system makes up record. In paper, we present integrated software tool associated...
In this paper we present an efficient GPU-based framework to dynamically perform the voxelization of polygonal models for training 3D convolutional neural networks. It is designed manage dataset augmentation by using geometric transformations and random vertex displacements in GPU. With proposed system, every carried out on-the-fly directly feeding network. The computing performance with approach much better than standard method, that carries each model separately has higher data processing...