Laboratoire Procédés et Ingénierie en Mécanique et Matériaux (PIMM): Recent submissions

Now showing items 344-350 of 1272

    • Spurious-free interpolations for non-intrusive PGD-based parametric solutions: Application to composites forming processes 
      Article dans une revue avec comité de lecture
      ccCUETO, Elias; FALCO, Antonio; DUVAL, Jean-Louis; ccGHNATIOS, Chady; ccCHINESTA SORIA, Francisco (Springer Science and Business Media LLC, 2020)
      Non-intrusive approaches for the construction of computational vademecums face different challenges, especially when a parameter variation affects the physics of the problem considerably. In these situations, classical ...
    • Learning data-driven reduced elastic and inelastic models of spot-welded patches 
      Article dans une revue avec comité de lecture
      REILLE, Agathe; CHAMPANEY, Victor; DAIM, Fatima; TOURBIER, Yves; HASCOET, Nicolas; GONZALEZ, David; ccCUETO, Elias; DUVAL, Jean Louis; ccCHINESTA SORIA, Francisco (EDP Sciences, 2021)
      Solving mechanical problems in large structures with rich localized behaviors remains a challenging issue despite the enormous advances in numerical procedures and computational performance. In particular, these localized ...
    • Numerical experiments on unsupervised manifold learning applied to mechanical modeling of materials and structures 
      Article dans une revue avec comité de lecture
      IBANEZ, Ruben; GILORMINI, Pierre; ccCUETO, Elias; ccCHINESTA 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 ...
    • From ROM of Electrochemistry to AI-Based Battery Digital and Hybrid Twin 
      Article dans une revue avec comité de lecture
      SANCARLOS, Abel; CAMERON, Morgan; ABEL, Andreas; ccCUETO, Elias; DUVAL, Jean-Louis; ccCHINESTA SORIA, Francisco (Springer Science and Business Media LLC, 2020)
      Lithium-ion batteries are widely used in the automobile industry (electric vehicles and hybrid electric vehicles) due to their high energy and power density. However, this raises new safety and reliability challenges which ...
    • Structure-preserving neural networks 
      Article dans une revue avec comité de lecture
      HERNÁNDEZ, Quercus; BADÍAS, Alberto; GONZÁLEZ, David; ccCHINESTA SORIA, Francisco; ccCUETO, Elias (Elsevier, 2021)
      We develop a method to learn physical systems from data that employs feedforward neural networks and whose predictions comply with the first and second principles of thermodynamics. The method employs a minimum amount of ...
    • Deep learning of thermodynamics-aware reduced-order models from data 
      Article dans une revue avec comité de lecture
      HERNANDEZ, Quercus; BADIAS, Alberto; GONZALEZ, David; ccCHINESTA SORIA, Francisco; ccCUETO, Elias (Elsevier, 2021)
      We present an algorithm to learn the relevant latent variables of a large-scale discretized physical system and predict its time evolution using thermodynamically-consistent deep neural networks. Our method relies on sparse ...
    • Real-Time Path Planning Based on Harmonic Functions under a Proper Generalized Decomposition-Based Framework 
      Article dans une revue avec comité de lecture
      MONTÉS, Nicolas; ccCHINESTA SORIA, Francisco; MORA, Marta C.; FALCÓ, Antonio; HILARIO, Lucia; ROSILLO, Nuria; NADAL, Enrique (MDPI AG, 2021)
      This paper presents a real-time global path planning method for mobile robots using harmonic functions, such as the Poisson equation, based on the Proper Generalized Decomposition (PGD) of these functions. The main property ...