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Machine-learning enhanced predictors for accelerated convergence of partitioned fluid-structure interaction simulationsComputer Physics Communications, 2025, 310, pp.109522. ⟨10.1016/j.cpc.2025.109522⟩
Article dans une revue
hal-05369767
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Non-intrusive reduced order models for partitioned fluid–structure interactionsJournal of Fluids and Structures, 2024, 128, pp.104156. ⟨10.1016/j.jfluidstructs.2024.104156⟩
Article dans une revue
hal-04624740
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Stratégies d'accélération de calcul numérique d'interaction fluide-structure par apprentissage automatiqueMathématiques générales [math.GM]. Conservatoire national des arts et metiers - CNAM, 2025. Français. ⟨NNT : 2025CNAM0008⟩
Thèse
tel-05269807
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Machine-Learning Enhanced Predictors for Accelerated Convergence of Partitioned Fluid-Structure Interaction Simulations2024
Pré-publication, Document de travail
hal-04576920
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Non-intrusive reduced order models for partitioned fluid-structure interactions2023
Pré-publication, Document de travail
(preprint/prepublication)
hal-04318891
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Non-intrusive reduced order models for partitioned fluid-structure interactionsERCOFTAC Symposium “Multiphysics critical flow dynamics involving moving/ deformable structures with design applications”, Jun 2023, Toulouse, France
Communication dans un congrès
hal-04120816
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