Elías Cueto

7.0k total citations · 1 hit paper
195 papers, 5.0k citations indexed

About

Elías Cueto is a scholar working on Statistical and Nonlinear Physics, Mechanics of Materials and Computational Mechanics. According to data from OpenAlex, Elías Cueto has authored 195 papers receiving a total of 5.0k indexed citations (citations by other indexed papers that have themselves been cited), including 89 papers in Statistical and Nonlinear Physics, 65 papers in Mechanics of Materials and 61 papers in Computational Mechanics. Recurrent topics in Elías Cueto's work include Model Reduction and Neural Networks (89 papers), Numerical methods in engineering (46 papers) and Advanced Numerical Methods in Computational Mathematics (31 papers). Elías Cueto is often cited by papers focused on Model Reduction and Neural Networks (89 papers), Numerical methods in engineering (46 papers) and Advanced Numerical Methods in Computational Mathematics (31 papers). Elías Cueto collaborates with scholars based in Spain, France and Nepal. Elías Cueto's co-authors include Francisco Chinesta, David González, Amine Ammar, Pierre Ladevèze, M. Doblaré, Icíar Alfaro, Emmanuelle Abisset‐Chavanne, José Vicente Aguado, Adrien Leygue and Antonio Huerta and has published in prestigious journals such as PLoS ONE, IEEE Transactions on Pattern Analysis and Machine Intelligence and Journal of Computational Physics.

In The Last Decade

Elías Cueto

191 papers receiving 4.9k citations

Hit Papers

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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.

A Short Review on Model Order Reduction Based on Proper Generalized Decomposition

2011 456 citations Francisco Chinesta, Elías Cueto et al. Archives of Computational Methods in Engineering profile →

Peers

Countries citing papers authored by Elías Cueto

Since Specialization

Citations

This map shows the geographic impact of Elías Cueto'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 Elías Cueto with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Elías Cueto more than expected).

Fields of papers citing papers by Elías Cueto

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Elías Cueto. 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 Elías Cueto. The network helps show where Elías Cueto may publish in the future.

Co-authorship network of co-authors of Elías Cueto

This figure shows the co-authorship network connecting the top 25 collaborators of Elías Cueto. A scholar is included among the top collaborators of Elías Cueto 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 Elías Cueto. Elías Cueto is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

#	Work	Indexed citations
1	Graph neural networks informed locally by thermodynamics 2025 ·Engineering Applications of Artificial Intelligence ·(unknown), Icíar Alfaro, David González G., Francisco Chinesta, Elías Cueto	4
2	Optimal trajectory planning combining model-based and data-driven hybrid approaches 2024 ·Advanced Modeling and Simulation in Engineering Sciences ·Chady Ghnatios, (unknown), (unknown), Amine Ammar, Elías Cueto, Francisco Chinesta	2
3	Physics-informed and graph neural networks for enhanced inverse analysis 2024 ·Engineering Computations ·(unknown), (unknown), Chady Ghnatios, Elías Cueto, Francisco Chinesta	3
4	Thermodynamics of Learning Physical Phenomena 2023 ·Archives of Computational Methods in Engineering ·Elías Cueto, Francisco Chinesta	22
5	Crossing Scales: Data-Driven Determination of the Micro-scale Behavior of Polymers From Non-homogeneous Tests at the Continuum-Scale 2022 ·Frontiers in Materials ·(unknown), Francisco J. Montáns, Elías Cueto, Francisco Chinesta	1
6	Modeling systems from partial observations 2022 ·Frontiers in Materials ·(unknown), (unknown), (unknown), (unknown), Chady Ghnatios, Francisco J. Montáns, Elías Cueto, Francisco Chinesta	3
7	Data Completion, Model Correction and Enrichment Based on Sparse Identification and Data Assimilation 2022 ·Applied Sciences ·(unknown), (unknown), (unknown), Elías Cueto, Francisco Chinesta	9
8	Empowering engineering with data, machine learning and artificial intelligence: a short introductive review 2022 ·Advanced Modeling and Simulation in Engineering Sciences ·Francisco Chinesta, Elías Cueto	17
9	MORPH-DSLAM: Model Order Reduction for Physics-Based Deformable SLAM 2021 ·IEEE Transactions on Pattern Analysis and Machine Intelligence ·Alberto Badías, Icíar Alfaro, David González, Francisco Chinesta, Elías Cueto	4
10	A novel sparse reduced order formulation for modeling electromagnetic forces in electric motors 2021 ·SN Applied Sciences ·(unknown), Elías Cueto, Francisco Chinesta, (unknown)	5
11	Physically sound, self-learning digital twins for sloshing fluids 2020 ·PLoS ONE ·(unknown), Icíar Alfaro, David González, Francisco Chinesta, Elías Cueto	16
12	A novel sensitivity analysis on friction spot joining process performed on aluminum\polycarbonate sheets by simulation 2020 ·International Journal of Material Forming ·(unknown), Francesco Gagliardi, (unknown), (unknown), Elías Cueto, Luigino Filice, Francisco Chinesta	1
13	An augmented reality platform for interactive aerodynamic design and analysis 2019 ·International Journal for Numerical Methods in Engineering ·Alberto Badías, (unknown), David González, Icíar Alfaro, Francisco Chinesta, Elías Cueto	17
14	Multiscale proper generalized decomposition based on the partition of unity 2019 ·International Journal for Numerical Methods in Engineering ·Rubén Ibáñez, Amine Ammar, Elías Cueto, Antonio Huerta, (unknown), Francisco Chinesta	5
15	Some applications of compressed sensing in computational mechanics: model order reduction, manifold learning, data-driven applications and nonlinear dimensionality reduction 2019 ·Computational Mechanics ·Rubén Ibáñez, Emmanuelle Abisset‐Chavanne, Elías Cueto, Amine Ammar, Jean Louis Duval, Francisco Chinesta	10
16	Hybrid constitutive modeling: data-driven learning of corrections to plasticity models 2018 ·International Journal of Material Forming ·Rubén Ibáñez, Emmanuelle Abisset‐Chavanne, David González, Jean Louis Duval, Elías Cueto, Francisco Chinesta	69
17	A physically-based fractional diffusion model for semi-dilute suspensions of rods in a Newtonian fluid 2017 ·Applied Mathematical Modelling ·E. Nadal, José Vicente Aguado, Emmanuelle Abisset‐Chavanne, Francisco Chinesta, R. Keunings, Elías Cueto	7
18	A proper generalized decomposition approach for high-order problems 2015 ·LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) ·(unknown), (unknown), David González, Icíar Alfaro, Adrien Leygue, (unknown), Elías Cueto, Francisco Chinesta	2
19	PGD-Based Computational Vademecum for Efficient Design, Optimization and Control 2013 ·Archives of Computational Methods in Engineering ·Francisco Chinesta, Adrien Leygue, Felipe Bordeu, José Vicente Aguado, Elías Cueto, David González, I. Alfaro, Amine Ammar, Antonio Huerta	222
20	10th Esaform Conference on Material Forming 2007 ·CERN Document Server (European Organization for Nuclear Research) ·Elías Cueto, Francisco Chinesta	12

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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