D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Engineering and Technology D-index 33 Citations 4,380 125 World Ranking 4279 National Ranking 113

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Mathematical analysis
  • Mechanical engineering

His main research concerns Curse of dimensionality, Algorithm, Mathematical optimization, Finite element method and Theoretical computer science. His work in Algorithm addresses issues such as Nonlinear system, which are connected to fields such as Simulation, Reduction and Real-time simulation. His Reduction study combines topics in areas such as Stochastic partial differential equation, Contrast, Proper generalized decomposition and Algebra.

Elías Cueto combines subjects such as Sensitivity, Interpolation, Meshfree methods, Discretization and Computational mechanics with his study of Mathematical optimization. His biological study spans a wide range of topics, including Basis function, Element, Point and Complex geometry. His Theoretical computer science research includes themes of Representation, Computation and Dimension.

His most cited work include:

  • A Short Review on Model Order Reduction Based on Proper Generalized Decomposition (395 citations)
  • Recent Advances and New Challenges in the Use of the Proper Generalized Decomposition for Solving Multidimensional Models (248 citations)
  • PGD-based “Computational Vademecum” for efficient design, optimization and control (201 citations)

What are the main themes of his work throughout his whole career to date?

His scientific interests lie mostly in Mathematical optimization, Applied mathematics, Model order reduction, Meshfree methods and Curse of dimensionality. His Mathematical optimization study integrates concerns from other disciplines, such as Computation, Numerical integration, Discretization, Numerical analysis and Computational mechanics. His work deals with themes such as Element, Boundary and Boundary value problem, which intersect with Applied mathematics.

His work investigates the relationship between Model order reduction and topics such as Real-time simulation that intersect with problems in Nonlinear system and Control theory. His work in Meshfree methods addresses issues such as Mechanical engineering, which are connected to fields such as Computer simulation. His Curse of dimensionality research includes elements of Algorithm, Representation and Theoretical computer science.

He most often published in these fields:

  • Mathematical optimization (18.04%)
  • Applied mathematics (17.65%)
  • Model order reduction (16.47%)

What were the highlights of his more recent work (between 2018-2021)?

  • Model order reduction (16.47%)
  • Algorithm (12.16%)
  • Curse of dimensionality (12.55%)

In recent papers he was focusing on the following fields of study:

Elías Cueto mainly investigates Model order reduction, Algorithm, Curse of dimensionality, Statistical physics and Nonlinear dimensionality reduction. His Model order reduction research is multidisciplinary, incorporating perspectives in Mathematical optimization, Visualization, Electric motor and Topological data analysis. In his works, Elías Cueto performs multidisciplinary study on Mathematical optimization and Distortion.

His studies in Algorithm integrate themes in fields like Transfer function, Representation, Simple, Compression and Cartesian coordinate system. Curse of dimensionality is a subfield of Artificial intelligence that Elías Cueto investigates. His Nonlinear dimensionality reduction study combines topics from a wide range of disciplines, such as State variable, Theoretical computer science, Manifold and Structural system.

Between 2018 and 2021, his most popular works were:

  • Virtual, Digital and Hybrid Twins: A New Paradigm in Data-Based Engineering and Engineered Data (44 citations)
  • Thermodynamically consistent data-driven computational mechanics (39 citations)
  • Hybrid constitutive modeling: data-driven learning of corrections to plasticity models (38 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Mathematical analysis
  • Mechanical engineering

His main research concerns Laws of thermodynamics, Dissipative system, Statistical physics, Dynamic mode decomposition and Human–computer interaction. His Laws of thermodynamics research spans across into fields like Calculus, Generalization, Physical law, Construct and Sign. His work carried out in the field of Statistical physics brings together such families of science as High dimensional and Free surface.

Elías Cueto incorporates a variety of subjects into his writings, including Human–computer interaction, SIMPLE, Statistical analysis, Viewpoints, Domain and Agile software development. Elías Cueto merges SIMPLE with Modeling and simulation in his study. His research integrates issues of Nanoindentation, Data-driven, Indentation and Curve fitting in his study of Modeling and simulation.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

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

Francisco Chinesta;Pierre Ladeveze;Elías Cueto.
Archives of Computational Methods in Engineering (2011)

580 Citations

Recent Advances and New Challenges in the Use of the Proper Generalized Decomposition for Solving Multidimensional Models

Francisco Chinesta;Amine Ammar;Elías Cueto.
Archives of Computational Methods in Engineering (2010)

439 Citations

PGD-based “Computational Vademecum” for efficient design, optimization and control

Francisco Chinesta;Adrien Leygue;Felipe Bordeu;Jose Vicente Aguado.
Archives of Computational Methods in Engineering (2013)

330 Citations

Overview and recent advances in natural neighbour galerkin methods

E. Cueto;N. Sukumar;B. Calvo;M. A. Martínez.
Archives of Computational Methods in Engineering (2003)

199 Citations

On the "a priori" model reduction: overview and recent developments

David Ryckelynck;Francisco Chinesta;Elías Cueto;Amine Ammar.
Archives of Computational Methods in Engineering (2006)

183 Citations

Recent advances on the use of separated representations

David González;Amine Ammar;Francisco Chinesta;Elías Cueto.
International Journal for Numerical Methods in Engineering (2009)

155 Citations

Real-time deformable models of non-linear tissues by model reduction techniques

S. Niroomandi;I. Alfaro;E. Cueto;F. Chinesta.
Computer Methods and Programs in Biomedicine (2008)

145 Citations

A Manifold Learning Approach to Data-Driven Computational Elasticity and Inelasticity

Rubén Ibañez;Emmanuelle Abisset-Chavanne;Jose Vicente Aguado;David Gonzalez.
Archives of Computational Methods in Engineering (2018)

118 Citations

Proper Generalized Decomposition based dynamic data-driven control of thermal processes ☆

Chady Ghnatios;Françoise Masson;Antonio Huerta;Adrien Leygue.
Computer Methods in Applied Mechanics and Engineering (2012)

114 Citations

Proper generalized decomposition of time-multiscale models

Amine Ammar;Francisco Chinesta;Elías Cueto;Manuel Doblaré.
International Journal for Numerical Methods in Engineering (2012)

102 Citations

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