David Pardo

David Pardo

Universidad del País Vasco

H-index: 33

Europe-Spain

About David Pardo

David Pardo, With an exceptional h-index of 33 and a recent h-index of 20 (since 2020), a distinguished researcher at Universidad del País Vasco, specializes in the field of Finite Element Methods, Solvers of linear equations, Geophysics, Deep Learning, Machine Learning.

His recent articles reflect a diverse array of research interests and contributions to the field:

r-Adaptive deep learning method for solving partial differential equations

Robust Variational Physics-Informed Neural Networks

Ensemble Deep Learning for enhanced seismic data reconstruction

Deep neural network for damage detection in Infante Dom Henrique bridge using multi-sensor data

Semi‐blind‐trace algorithm for self‐supervised attenuation of trace‐wise coherent noise

Adaptive Deep Fourier Residual method via overlapping domain decomposition

Learning quantities of interest from parametric PDEs: An efficient neural-weighted Minimal Residual approach

Multidirectional deep learning for data reconstruction

David Pardo Information

University

Position

Ikerbasque (UPV/EHU) and BCAM

Citations(all)

5915

Citations(since 2020)

2598

Cited By

4123

hIndex(all)

33

hIndex(since 2020)

20

i10Index(all)

102

i10Index(since 2020)

55

Email

University Profile Page

Universidad del País Vasco

Google Scholar

View Google Scholar Profile

David Pardo Skills & Research Interests

Finite Element Methods

Solvers of linear equations

Geophysics

Deep Learning

Machine Learning

Top articles of David Pardo

Title

Journal

Author(s)

Publication Date

r-Adaptive deep learning method for solving partial differential equations

Computers & Mathematics with Applications

Ángel J Omella

David Pardo

2024/1/1

Robust Variational Physics-Informed Neural Networks

Computer Methods in Applied Mechanics and Engineering

Sergio Rojas

Paweł Maczuga

Judit Muñoz-Matute

David Pardo

Maciej Paszyński

2024/5/15

Ensemble Deep Learning for enhanced seismic data reconstruction

arXiv preprint arXiv:2404.02632

Mohammad Mahdi Abedi

David Pardo

Tariq Alkhalifah

2024/4/3

Deep neural network for damage detection in Infante Dom Henrique bridge using multi-sensor data

Structural Health Monitoring

Ana Fernandez-Navamuel

David Pardo

Filipe Magalhães

Diego Zamora-Sánchez

Ángel J Omella

...

2024/3/22

Semi‐blind‐trace algorithm for self‐supervised attenuation of trace‐wise coherent noise

Geophysical Prospecting

Mohammad Mahdi Abedi

David Pardo

Tariq Alkhalifah

2024/2/21

Adaptive Deep Fourier Residual method via overlapping domain decomposition

arXiv preprint arXiv:2401.04663

Jamie M Taylor

Manuela Bastidas

Victor M Calo

David Pardo

2024/1/9

Learning quantities of interest from parametric PDEs: An efficient neural-weighted Minimal Residual approach

arXiv preprint arXiv:2304.01722

Ignacio Brevis

Ignacio Muga

David Pardo

Oscar Rodríguez

Kristoffer G van der Zee

2023/4/4

Multidirectional deep learning for data reconstruction

MM Abedi

D Pardo

2023/6/5

A deep double Ritz method (D2RM) for solving partial differential equations using neural networks

Computer Methods in Applied Mechanics and Engineering

Carlos Uriarte

David Pardo

Ignacio Muga

Judit Muñoz-Matute

2023/2/15

Physics-guided deep-learning inversion method for the interpretation of noisy logging-while-drilling resistivity measurements

Geophysical Journal International

Kyubo Noh

David Pardo

Carlos Torres-Verdín

2023/10

Multi-blind-trace deep learning with a hybrid loss for attenuation of trice-wise noise

Mohammad Mahdi Abedi

David Pardo

Tariq Alkhalifah

2023/6/5

Bridge damage identification under varying environmental and operational conditions combining Deep Learning and numerical simulations

Mechanical Systems and Signal Processing

Ana Fernandez-Navamuel

David Pardo

Filipe Magalhães

Diego Zamora-Sánchez

Ángel J Omella

...

2023/10/1

A deep Fourier residual method for solving PDEs using neural networks

Computer Methods in Applied Mechanics and Engineering

Jamie M Taylor

David Pardo

Ignacio Muga

2023/2/15

Multimodal variational autoencoder for inverse problems in geophysics: application to a 1-D magnetotelluric problem

Geophysical Journal International

Oscar Rodriguez

Jamie M Taylor

David Pardo

2023/12

mahdiabedi/semi-blind-trace-deep-learning

Mohammad Mahdi Abedi

David Pardo

Tariq Ali Alkhalifah

2023/5/21

Neural network architecture optimization using automated machine learning for borehole resistivity measurements

Geophysical Journal International

Mostafa Shahriari

David Pardo

S Kargaran

Tomás Teijeiro

2023/9

Fast parallel IGA-ADS solver for time-dependent Maxwell's equations

Computers & Mathematics with Applications

Marcin Łoś

Maciej Woźniak

Keshav Pingali

Luis Emilio Garcia Castillo

Julen Alvarez-Aramberri

...

2023/12/1

Deep Fourier Residual method for solving time-harmonic Maxwell's equations

arXiv preprint arXiv:2305.09578

Jamie M Taylor

Manuela Bastidas

David Pardo

Ignacio Muga

2023/5/16

Semi-blind-trace algorithm for self-supervised attenuation of trace-wise coherent noise

arXiv e-prints

Mohammad Mahdi Abedi

David Pardo

Tariq Alkhalifa

2023/8

Diagnosis of the health status of mooring systems for floating offshore wind turbines using autoencoders

Ocean Engineering

N Gorostidi

D Pardo

V Nava

2023/11/1

See List of Professors in David Pardo University(Universidad del País Vasco)

Co-Authors

H-index: 43
Victor M. Calo

Victor M. Calo

Curtin University

H-index: 38
Marcelo Bertalmío

Marcelo Bertalmío

Universidad Pompeu Fabra

H-index: 30
Lisandro Dalcin

Lisandro Dalcin

King Abdullah University of Science and Technology

H-index: 28
Adrian Galdran

Adrian Galdran

Bournemouth University

H-index: 27
Maciej Paszynski

Maciej Paszynski

Akademia Górniczo-Hutnicza

H-index: 18
Javier Vazquez-Corral (ORCID: 0000-0003-0414-7096)

Javier Vazquez-Corral (ORCID: 0000-0003-0414-7096)

Universidad Pompeu Fabra

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