Jamie M. Taylor

According to our database, Jamie M. Taylor authored at least 17 papers between 2018 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2026

Robust Deep FOSLS for Transmission Problems.

Alejandro Duque, Paulina Sepúlveda, Carlos Uriarte, Jamie M. Taylor, David Pardo

CoRR, April, 2026

RUNNs: Ritz-Uzawa Neural Networks for Solving Variational Problems.

Pablo Herrera, Jamie M. Taylor, Carlos Uriarte, Ignacio Muga, David Pardo, Kristoffer G. van der Zee

CoRR, March, 2026

A Least-Squares-Based Regularity-Conforming Neural Networks (LS-ReCoNNs) for Solving Parametric Transmission Problems.

Shima Baharlouei, Jamie M. Taylor, David Pardo

CoRR, March, 2026

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

J. Comput. Phys., 2026

Stochastic quadrature rules for solving PDEs using neural networks.

Jamie M. Taylor, David Pardo

Comput. Math. Appl., 2026

2025

Regularity-conforming neural networks (ReCoNNs) for solving partial differential equations.

Jamie M. Taylor, David Pardo, Judit Muñoz-Matute

J. Comput. Phys., 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

Optimizing Variational Physics-Informed Neural Networks Using Least Squares.

Carlos Uriarte, Manuela Bastidas, David Pardo, Jamie M. Taylor, Sergio Rojas

Comput. Math. Appl., 2025

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

Adaptive Deep Fourier Residual method via overlapping domain decomposition.

Jamie M. Taylor, Manuela Bastidas, Victor M. Calo, David Pardo

CoRR, 2024

2023

Machine Learning Discovery of Optimal Quadrature Rules for Isogeometric Analysis.

Tomás Teijeiro, Jamie M. Taylor, Ali Hashemian, David Pardo

CoRR, 2023

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

Proceedings of the Computational Science - ICCS 2023, 2023

2022

A Deep Fourier Residual Method for solving PDEs using Neural Networks.

Jamie M. Taylor, David Pardo, Ignacio Muga

CoRR, 2022

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

On quadrature rules for solving Partial Differential Equations using Neural Networks.

Jon Ander Rivera, Jamie M. Taylor, Angel Javier Omella, David Pardo

CoRR, 2021

2019

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

2018

An Analysis of Equilibria in Dense Nematic Liquid Crystals.

Jamie M. Taylor

SIAM J. Math. Anal., 2018