An Analysis of Equilibria in Dense Nematic Liquid Crystals. - Jamie M. Taylor (SIAM J. Math. Anal., 2018)

Convex Integration Arising in the Modelling of Shape-Memory Alloys: Some Remarks on Rigidity, Flexibility and Some Numerical Implementations. - Angkana Rüland; Jamie M. Taylor; Christian Zillinger (J. Nonlinear Sci., 2019)

On quadrature rules for solving Partial Differential Equations using Neural Networks. - Jon Ander Rivera; Jamie M. Taylor; Angel Javier Omella; David Pardo (CoRR, 2021)

Cavity Volume and Free Energy in Many-Body Systems. - Jamie M. Taylor; Thomas G. Fai; Epifanio G. Virga; Xiaoyu Zheng; Peter Palffy-Muhoray (J. Nonlinear Sci., 2021)

A Deep Fourier Residual Method for solving PDEs using Neural Networks. - Jamie M. Taylor; David Pardo; Ignacio Muga (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)

Machine Learning Discovery of Optimal Quadrature Rules for Isogeometric Analysis. - Tomás Teijeiro; Jamie M. Taylor; Ali Hashemian; David Pardo (CoRR, 2023)

Adaptive Deep Fourier Residual method via overlapping domain decomposition. - Jamie M. Taylor; Manuela Bastidas; Victor M. Calo; David Pardo (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)

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

Deep Fourier Residual method for solving time-harmonic Maxwell's equations. - Jamie M. Taylor; Manuela Bastidas; David Pardo; Ignacio Muga (J. Comput. Phys., 2025)

Regularity-conforming neural networks (ReCoNNs) for solving partial differential equations. - Jamie M. Taylor; David Pardo; Judit Muñoz-Matute (J. Comput. Phys., 2025)

Stochastic Quadrature Rules for Solving PDEs using Neural Networks. - Jamie M. Taylor; David Pardo (CoRR, 2025)

Efficient Numerical Integration for Finite Element Trunk Spaces in 2D and 3D using Machine Learning: A new Optimisation Paradigm to Construct Application-Specific Quadrature Rules. - Tomás Teijeiro; Pouria Behnoudfar; Jamie M. Taylor; David Pardo; Victor M. Calo (CoRR, 2025)