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Taylor, Jamie Michael
Mathematics
PhD: University of Oxford
Office D3-27
Biography
Jamie Taylor is an Assistant Professor on Tenure Track in the Department of Mathematics at CUNEF Universidad. He has developed a distinguished research career in applied mathematics, recognized with a sexenio for the period 2013-2018 (Field 1 - Mathematics and Physics). He has published extensively in high-impact indexed journals, with the majority of his work appearing in top-quartile and top-decile journals. He is currently co-principal investigator of the national project "UltraPINNs for Enhanced Diagnostic Imaging." He holds a PhD in Mathematics from the University of Oxford under the supervision of Sir John Ball. He has also been a speaker at international conferences, a reviewer for several JCR-indexed journals, and a member of PhD thesis committees.
Education
PhD in Mathematics, University of Oxford (2017)
MMath in Mathematics, Cardiff University (2012)
Research Interests
Numerical methods for solving partial differential equations, with a focus on parametric problems using machine learning techniques.
Most relevant publications
Baharlouei, S., Taylor, J. M., Uriarte, C., & Pardo, D. (2025). A least-squares-based neural network (LS-Net) for solving linear parametric PDEs., Computer Methods in Applied Mechanics and Engineering, 437, 117757.
Taylor, J. M., Bastidas, M., Pardo, D., & Muga, I. (2025). Deep Fourier Residual method for solving time-harmonic Maxwell's equations., Journal of Computational Physics, 523, 113623.
Taylor, J. M., Bastidas, M., Calo, V. M., & Pardo, D. (2024). Adaptive Deep Fourier Residual method via overlapping domain decomposition., Computer Methods in Applied Mechanics and Engineering, 427, 116997.
Taylor, J. M., Pardo, D., & Muga, I. (2023). A deep Fourier residual method for solving PDEs using neural networks., Computer Methods in Applied Mechanics and Engineering, 405, 115850.
Rivera, J. A., Taylor, J. M., Omella, Á. J., & Pardo, D. (2022). On quadrature rules for solving partial differential equations using neural networks., Computer Methods in Applied Mechanics and Engineering, 393, 114710.
Otros profesores
