Foreword

CUETO, Elias; LADEVÈZE, Pierre; CHINESTA SORIA, Francisco

Article dans une revue avec comité de lecture

Auteur

CUETO, Elias
95355 Universidad de Zaragoza = University of Zaragoza [Saragossa University] = Université de Saragosse

LADEVÈZE, Pierre
523723 Ecole Normale Supérieure Paris-Saclay [ENS Paris Saclay]

CHINESTA SORIA, Francisco
86289 Laboratoire Procédés et Ingénierie en Mécanique et Matériaux [PIMM]

URI

http://hdl.handle.net/10985/20601

DOI

10.1016/j.crme.2019.11.016

Date

2019

Journal

Comptes Rendus Mécanique

Résumé

No abstract

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Format:: PDF

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Laboratoire Procédés et Ingénierie en Mécanique et Matériaux (PIMM)

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Numerical experiments on unsupervised manifold learning applied to mechanical modeling of materials and structures

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IBANEZ, Ruben; GILORMINI, Pierre; CUETO, Elias; CHINESTA SORIA, Francisco (Elsevier Masson, 2020)

The present work aims at analyzing issues related to the data manifold dimensionality. The interest of the study is twofold: (i) first, when too many measurable variables are considered, manifold learning is expected to ...
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Article dans une revue avec comité de lecture

AMORES, Víctor J.; MONTÁNS, Francisco J.; CUETO, Elias; CHINESTA SORIA, Francisco (Frontiers Media SA, 2022-05)

We propose an efficient method to determine the micro-structural entropic behavior of polymer chains directly from a sufficiently rich non-homogeneous experiment at the continuum scale. The procedure is developed in 2 ...
Thermodynamics-informed neural networks for physically realistic mixed reality

Article dans une revue avec comité de lecture

HERNÁNDEZ, Quercus; BADIAS, Alberto; CHINESTA SORIA, Francisco; CUETO, Elias (2023)

The imminent impact of immersive technologies in society urges for active research in real-time and interactive physics simulation for virtual worlds to be realistic. In this context, realistic means to be compliant to the ...
Regularized regressions for parametric models based on separated representations

Article dans une revue avec comité de lecture

SANCARLOS, Abel; CHAMPANEY, Victor; CUETO, Elias; CHINESTA SORIA, Francisco (Springer Open, 2023-03)

Regressions created from experimental or simulated data enable the construction of metamodels, widely used in a variety of engineering applications. Many engineering problems involve multi-parametric physics whose corresponding ...
Port-metriplectic neural networks: thermodynamics-informed machine learning of complex physical systems

Article dans une revue avec comité de lecture

HERNÁNDEZ, Quercus; BADIAS, Alberto; CHINESTA SORIA, Francisco; CUETO, Elias (2023)

We develop inductive biases for the machine learning of complex physical systems based on the port-Hamiltonian formalism. To satisfy by construction the principles of thermodynamics in the learned physics (conservation of ...