David Pardo

5.1k total citations · 1 hit paper
182 papers, 3.5k citations indexed

About

David Pardo is a scholar working on Geophysics, Ocean Engineering and Computational Mechanics. According to data from OpenAlex, David Pardo has authored 182 papers receiving a total of 3.5k indexed citations (citations by other indexed papers that have themselves been cited), including 77 papers in Geophysics, 64 papers in Ocean Engineering and 60 papers in Computational Mechanics. Recurrent topics in David Pardo's work include Geophysical and Geoelectrical Methods (63 papers), Geophysical Methods and Applications (54 papers) and Advanced Numerical Methods in Computational Mathematics (49 papers). David Pardo is often cited by papers focused on Geophysical and Geoelectrical Methods (63 papers), Geophysical Methods and Applications (54 papers) and Advanced Numerical Methods in Computational Mathematics (49 papers). David Pardo collaborates with scholars based in Spain, United States and Poland. David Pardo's co-authors include Adrián Galdrán, Leszek Demkowicz, Carlos Torres‐Verdín, Artzai Picón, Maciej Paszyński, Aitor Álvarez-Gila, Victor M. Calo, W. Rachowicz, Jason Kurtz and Adam Zdunek and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of Computational Physics and IEEE Transactions on Geoscience and Remote Sensing.

In The Last Decade

David Pardo

172 papers receiving 3.4k citations

Hit Papers

Automatic Red-Channel underwater image restoration 2014 2026 2018 2022 2014 200 400 600
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Automatic Red-Channel underwater image restoration
    2014 725 citations David Pardo et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
David Pardo Spain 27 1.1k 903 786 722 657 182 3.5k
Giacomo Oliveri Italy 42 863 0.8× 139 0.2× 109 0.1× 873 1.2× 3.6k 5.5× 227 6.6k
Bangti Jin United Kingdom 36 692 0.6× 250 0.3× 200 0.3× 97 0.1× 474 0.7× 140 4.5k
Paolo Rocca Italy 44 546 0.5× 169 0.2× 71 0.1× 657 0.9× 4.2k 6.4× 292 7.5k
Dan E. Dudgeon United States 13 635 0.6× 620 0.7× 127 0.2× 147 0.2× 473 0.7× 37 2.8k
Gerlind Plonka Germany 23 559 0.5× 1.1k 1.2× 271 0.3× 81 0.1× 67 0.1× 95 1.8k
Y. C. Pati United States 10 1.6k 1.4× 1.3k 1.5× 79 0.1× 99 0.1× 752 1.1× 26 3.6k
Xiang Li China 33 852 0.8× 385 0.4× 38 0.0× 256 0.4× 790 1.2× 338 4.2k
O. Hassan United Kingdom 28 1.8k 1.6× 180 0.2× 77 0.1× 163 0.2× 445 0.7× 128 3.1k
Michael Hintermüller Germany 32 1.7k 1.5× 716 0.8× 100 0.1× 50 0.1× 127 0.2× 135 3.3k
Andreas Kirsch Germany 35 314 0.3× 162 0.2× 323 0.4× 704 1.0× 771 1.2× 106 4.9k

Countries citing papers authored by David Pardo

Since Specialization
Citations

This map shows the geographic impact of David Pardo's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by David Pardo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Pardo more than expected).

Fields of papers citing papers by David Pardo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David Pardo. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by David Pardo. The network helps show where David Pardo may publish in the future.

Co-authorship network of co-authors of David Pardo

This figure shows the co-authorship network connecting the top 25 collaborators of David Pardo. A scholar is included among the top collaborators of David Pardo based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with David Pardo. David Pardo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Pardo, David, et al.. (2025). Optimizing Variational Physics-Informed Neural Networks Using Least Squares. Computers & Mathematics with Applications. 185. 76–93. 3 indexed citations
2.
Rojas, Sergio, et al.. (2024). Robust Variational Physics-Informed Neural Networks. Computer Methods in Applied Mechanics and Engineering. 425. 116904–116904. 17 indexed citations
3.
Muga, Ignacio, et al.. (2024). Learning quantities of interest from parametric PDEs: An efficient neural-weighted Minimal Residual approach. Computers & Mathematics with Applications. 164. 139–149. 3 indexed citations
4.
Pardo, David, et al.. (2024). Residual-based attention Physics-informed Neural Networks for spatio-temporal ageing assessment of transformers operated in renewable power plants. Engineering Applications of Artificial Intelligence. 139. 109556–109556. 5 indexed citations
5.
Pardo, David, et al.. (2024). Deep neural network for damage detection in Infante Dom Henrique bridge using multi-sensor data. Structural Health Monitoring. 24(1). 372–401. 8 indexed citations
6.
Pardo, David, et al.. (2024). Deep Fourier Residual method for solving time-harmonic Maxwell's equations. Journal of Computational Physics. 523. 113623–113623. 2 indexed citations
7.
Calo, Victor M., et al.. (2024). Adaptive Deep Fourier Residual method via overlapping domain decomposition. Computer Methods in Applied Mechanics and Engineering. 427. 116997–116997. 7 indexed citations
8.
Pardo, David, et al.. (2023). Diagnosis of the health status of mooring systems for floating offshore wind turbines using autoencoders. Ocean Engineering. 287. 115862–115862. 14 indexed citations
9.
Pardo, David, et al.. (2023). Bridge damage identification under varying environmental and operational conditions combining Deep Learning and numerical simulations. Mechanical Systems and Signal Processing. 200. 110471–110471. 21 indexed citations
10.
Teijeiro, Tomás, et al.. (2023). Machine learning discovery of optimal quadrature rules for isogeometric analysis. Computer Methods in Applied Mechanics and Engineering. 416. 116310–116310. 3 indexed citations
11.
Shahriari, M., et al.. (2023). Neural network architecture optimization using automated machine learning for borehole resistivity measurements. Geophysical Journal International. 234(3). 2487–2500.
12.
Pardo, David, et al.. (2023). Physics-guided deep-learning inversion method for the interpretation of noisy logging-while-drilling resistivity measurements. Geophysical Journal International. 235(1). 150–165. 5 indexed citations
13.
Magalhães, Filipe, et al.. (2022). Deep learning enhanced principal component analysis for structural health monitoring. Structural Health Monitoring. 21(4). 1710–1722. 34 indexed citations
14.
Pardo, David, et al.. (2022). Supervised Deep Learning with Finite Element simulations for damage identification in bridges. Engineering Structures. 257. 114016–114016. 56 indexed citations
15.
Pardo, David, et al.. (2022). On quadrature rules for solving Partial Differential Equations using Neural Networks. Computer Methods in Applied Mechanics and Engineering. 393. 114710–114710. 26 indexed citations
16.
Hashemian, Ali, et al.. (2022). Refined isogeometric analysis of quadratic eigenvalue problems. Computer Methods in Applied Mechanics and Engineering. 399. 115327–115327. 4 indexed citations
17.
Pardo, David, et al.. (2022). A Deep Fourier Residual method for solving PDEs using Neural Networks. Computer Methods in Applied Mechanics and Engineering. 405. 115850–115850. 22 indexed citations
18.
García-Sánchez, David, et al.. (2020). Bearing assessment tool for longitudinal bridge performance. Journal of Civil Structural Health Monitoring. 10(5). 1023–1036. 20 indexed citations
19.
Ortega, Jaime H., et al.. (2018). Source time reversal (STR) method for linear elasticity. Computers & Mathematics with Applications. 77(5). 1358–1375.
20.
Pardo, David, et al.. (2002). Compact embedding of a degenerate Sobolev space and existence of entire solutions to a semilinear equation for a Grushin-type operator. Rendiconti del Seminario Matematico della Università di Padova. 107. 139–152. 5 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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