German Castellanos-Dominguez

Jul 16, 2024 | mdpi.com | Andrés Marino Álvarez-Meza |German Castellanos-Dominguez |David Augusto Cárdenas-Peña |Cristian Alejandro Blanco-Martínez

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.

Jun 17, 2024 | preprints.org | Andrés Marino Álvarez-Meza |German Castellanos-Dominguez |Diego Armando Perez-Rosero

PreprintArticleVersion 1This version is not peer-reviewedVersion 1: Received: 17 June 2024 / Approved: 17 June 2024 / Online: 17 June 2024 (08:14:01 CEST)Perez-Rosero, D. A.; Álvarez-Meza, A. M.; Castellanos-Dominguez, G. A Regularized Physics-Informed Neural Network to support Data-Driven Nonlinear Constrained Optimization. Preprints 2024, 2024061118. https://doi.org/10.20944/preprints202406.1118.v1Perez-Rosero, D. A.; Álvarez-Meza, A. M.; Castellanos-Dominguez, G.

May 4, 2023 | frontiersin.org | Stefan Haufe |Ji Chen |German Castellanos-Dominguez |Luis Gomez |Luis Gómez

1. Introduction Electroencephalography (EEG) is a widely used tool for the assessment of neural activity in the human brain (Brette and Destexhe, 2012). To estimate the area of the brain responsible for the measured data, one has to simulate the electric potential as induced by hypothetical current sources in the brain, i.e., the EEG forward problem has to be solved.