R
Rubén Ibáñez
Researcher at École centrale de Nantes
Publications - 23
Citations - 545
Rubén Ibáñez is an academic researcher from École centrale de Nantes. The author has contributed to research in topics: Nonlinear dimensionality reduction & Model order reduction. The author has an hindex of 7, co-authored 23 publications receiving 338 citations. Previous affiliations of Rubén Ibáñez include University of Paris & Polytechnic University of Catalonia.
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A Manifold Learning Approach to Data-Driven Computational Elasticity and Inelasticity
Rubén Ibáñez,Emmanuelle Abisset-Chavanne,Jose Vicente Aguado,David González,Elías Cueto,Francisco Chinesta +5 more
TL;DR: This work proposes a new method, able to directly link data to computers in order to perform numerical simulations that will employ axiomatic, universal laws while minimizing the need of explicit, often phenomenological, models.
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Data-driven non-linear elasticity: constitutive manifold construction and problem discretization
Rubén Ibáñez,Domenico Borzacchiello,Jose Vicente Aguado,Emmanuelle Abisset-Chavanne,Elías Cueto,Pierre Ladevèze,Francisco Chinesta +6 more
TL;DR: Data-Driven simulation constitutes a potential change of paradigm in SBES by using a data-driven inverse approach in order to generate the whole constitutive manifold from few complex experimental tests, as discussed in the present work.
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Hybrid constitutive modeling: data-driven learning of corrections to plasticity models
Rubén Ibáñez,Emmanuelle Abisset-Chavanne,David González,Jean Louis Duval,Elías Cueto,Francisco Chinesta +5 more
TL;DR: This work proposes to develop correction to those popular models so as to minimize the errors in constitutive modeling by means of machine learning techniques.
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A Multidimensional Data-Driven Sparse Identification Technique: The Sparse Proper Generalized Decomposition
Rubén Ibáñez,Emmanuelle Abisset-Chavanne,Amine Ammar,David González,Elías Cueto,Antonio Huerta,Jean Louis Duval,Francisco Chinesta +7 more
TL;DR: This work presents a technique based on the same principles of the Proper Generalized Decomposition that enables the identification of complex laws in the low-data limit and provides examples on the performance of the technique in up to ten dimensions.
Journal ArticleDOI
Data-Driven Computational Plasticity
Francisco Chinesta,Pierre Ladevèze,Rubén Ibáñez,Jose Vicente Aguado,Emmanuelle Abisset-Chavanne,Elías Cueto +5 more
TL;DR: This work revisits its former work on data-driven computational linear and nonlinear elasticity and the rationale is extended for addressing computational inelasticity (viscoelastoplasticity).