A Deep Double Ritz Method (D2RM) for solving Partial Differential Equations using Neural Networks. - Carlos Uriarte; David Pardo; Ignacio Muga; Judit Muñoz-Matute (CoRR, 2022)

Memory-Based Monte Carlo Integration for Solving Partial Differential Equations Using Neural Networks. - Carlos Uriarte; Jamie M. Taylor; David Pardo; Oscar A. Rodríguez; Patrick Vega (ICCS (2), 2023)

Accelerating Training of Physics Informed Neural Network for 1D PDEs with Hierarchical Matrices. - Mateusz Dobija; Anna Paszynska; Carlos Uriarte; Maciej Paszynski (ICCS (3), 2024)

Solving Partial Differential Equations Using Artificial Neural Networks. - Carlos Uriarte (CoRR, 2024)

A Least-Squares-Based Neural Network (LS-Net) for Solving Linear Parametric PDEs. - Shima Baharlouei; Jamie M. Taylor; Carlos Uriarte; David Pardo (CoRR, 2024)

Tensorial Implementation for Robust Variational Physics-Informed Neural Networks. - Askold Vilkha; Carlos Uriarte; Pawel Maczuga; Tomasz Sluzalec; Maciej Paszynski (ICCS (1), 2025)

Optimizing Variational Physics-Informed Neural Networks Using Least Squares. - Carlos Uriarte; Manuela Bastidas; David Pardo; Jamie M. Taylor; Sergio Rojas (Comput. Math. Appl., 2025)

An r-adaptive finite element method using neural networks for parametric self-adjoint elliptic problem. - Danilo Aballay; Federico Fuentes; Vicente Iligaray; Angel Javier Omella; David Pardo; Manuel A. Sánchez; Ignacio Tapia; Carlos Uriarte (CoRR, 2025)