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Ignacio Brevis
Ignacio Brevis
Research Fellow, University of Nottingham
Adresse e-mail validée de nottingham.ac.uk - Page d'accueil
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A machine-learning minimal-residual (ML-MRes) framework for goal-oriented finite element discretizations
I Brevis, I Muga, KG van der Zee
Computers & Mathematics with Applications 95, 186-199, 2021
412021
Neural control of discrete weak formulations: Galerkin, least squares & minimal-residual methods with quasi-optimal weights
I Brevis, I Muga, KG van der Zee
Computer Methods in Applied Mechanics and Engineering 402, 115716, 2022
172022
Data-driven finite elements methods: Machine learning acceleration of goal-oriented computations
I Brevis, I Muga, KG van der Zee
arXiv preprint arXiv:2003.04485, 2020
132020
A source time reversal method for seismicity induced by mining
RI Brevis, JH Ortega, D Pardo
Inverse Problems and Imaging 11 (1), 25-45, 2017
52017
Learning quantities of interest from parametric PDEs: An efficient neural-weighted Minimal Residual approach
I Brevis, I Muga, D Pardo, O Rodriguez, KG Van Der Zee
Computers & Mathematics with Applications 164, 139-149, 2024
42024
Source time reversal (STR) method for linear elasticity
I Brevis, Á Rodríguez-Rozas, JH Ortega, D Pardo
Computers & Mathematics with Applications 77 (5), 1358-1357, 2018
12018
Controlling ZIF-67 film properties in water-based cathodic electrochemical deposition
E Elsayed, I Brevis, S Pandiyan, R Wildman, KG van der Zee, B Tokay
Journal of Solid State Chemistry 338, 124820, 2024
2024
Inverse source problems for coupled parabolic systems from measurements of one internal component
C Montoya, I Brevis, D Bolivar
arXiv preprint arXiv:2402.07593, 2024
2024
Source time reversal methods for acoustic and elastic waves
RI Brevis Vergara
Universidad de Chile, 2018
2018
Neural Control of Finite Element Methods: Quasi-optimal convergence of quasi-minimizing neural networks
I Brevis, I Muga, KG van der Zee
BOOK OF, 127, 0
A MACHINE LEARNING LEAST-SQUARES METHOD WITH A WEIGHTED NORM.
I BREVIS, I MUGA, P SEPÚLVEDA
Data-Driven Goal-Oriented Finite Element Methods: A Machine-Learning Minimal-Residual (ML-MRes) Framework
KG van der Zee, I Brevis, I Muga
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