| A Finite Element based Deep Learning solver for parametric PDEs C Uriarte, D Pardo, ÁJ Omella Computer Methods in Applied Mechanics and Engineering 391, 114562, 2022 | 23 | 2022 |
| A deep double Ritz method (D2RM) for solving partial differential equations using neural networks C Uriarte, D Pardo, I Muga, J Muñoz-Matute Computer Methods in Applied Mechanics and Engineering 405, 115892, 2023 | 9 | 2023 |
| Memory-Based Monte Carlo Integration for Solving Partial Differential Equations Using Neural Networks C Uriarte, JM Taylor, D Pardo, OA Rodríguez, P Vega International Conference on Computational Science, 509-516, 2023 | 1 | 2023 |
| Solving Partial Differential Equations Using Artificial Neural Networks C Uriarte arXiv preprint arXiv:2403.09001, 2024 | | 2024 |
| Solving Partial Differential Equations using Adversarial Neural Networks C Uriarte, D Pardo, JM Matute, I Muga Congress on NumericalMethods in Engineering CMN 2022 (2022. Las Palmas de …, 2022 | | 2022 |
| Massive finite element computations for deep learning inversion C Uriarte Baranda | | 2019 |
| La Función Zeta de Riemann y su relación con la distribución de los números primos C Uriarte Baranda | | 2018 |
| VARIATIONAL PHYSICS-INFORMED NEURAL NETWORKS OPTIMIZED WITH LEAST SQUARES AND ADAPTIVITY IN THE TEST SPACE M BASTIDAS, C URIARTE, JM TAYLOR, S ROJAS, VM CALO, D PARDO | | |
David PardoIkerbasque, University of the Basque Country (UPV/EHU), and BCAMVerified email at ehu.eus
