Publications
Publications
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Noise-bias compensation for the unsupervised learning of constitutive lawsComptes Rendus. Mécanique, 2026, 354, pp.1-24. ⟨10.5802/crmeca.342⟩ |
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Experimental Learning of a Hyperelastic Behavior with a Physics-Augmented Neural NetworkExperimental Mechanics, 2024, ⟨10.1007/s11340-024-01106-5⟩ |
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Unsupervised learning of history-dependent constitutive material laws with thermodynamically-consistent neural networks in the modified Constitutive Relation Error framework.Computer Methods in Applied Mechanics and Engineering, 2024, 425, pp.116967. ⟨10.1016/j.cma.2024.116967⟩ |
Intégrer les connaissances physiques dans les réseaux de neurones : application à l’apprentissage des lois de comportement matériaux à partir de mesures de déformation par fibres optiquesLa Revue 3E.I, 2022, 109 |
Unsupervised learning of constitutive model with neural networks and sparse identification of internal variables3rd IACM Digital Twins in Engineering Conference (DTE 2025) & 1st ECCOMAS Artificial Intelligence and Computational Methods in Applied Science (AICOMAS 2025), Feb 2025, Paris, France |
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Training an AI hyperelastic constitutive model with experimental dataPhotoMechanics - iDICs 2024, Oct 2024, Clermont - Ferrand, France |
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Apprentissage non-supervisé de lois de comportement non- linéaires avec réseau de neurones thermodynamiquement consistent par minimisation de l'erreur en relation de comportement modifiée16ème Colloque National en Calcul de Structures (CSMA 2024), CNRS; CSMA; ENS Paris-Saclay; CentraleSupélec, May 2024, Hyères, France |
Use of the modified constitutive relation error to learn constitutive relationsECCM21 - 21st European Conference on Composite Materials, Jul 2024, Nantes, France |
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Adaptive modeling and learning of material laws for effective data assimilation16th World Congress on Computational Mechanics and 4th Pan American Congress on Computational Mechanics (WCCM-PANAM 2024), Jul 2024, Vancouver (BC), Canada |
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Physics-Augmented Neural Networks for Constitutive Modeling: Toward an Application for Structural Health MonitoringECCOMAS Congress 2024 - The 9th European Congress on Computational Methods in Applied Sciences and Engineering, ECCOMAS Congress 2024, Jun 2024, Lisbon, Portugal |
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A modified Constitutive Relation Error (mCRE) framework to learn nonlinear constitutive models from strain measurements with thermodynamics-consistent Neural NetworksXI Conference on Adaptive Modeling and Simulation, Jun 2023, Gothenburg, France |
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A modified Constitutive Relation Error framework to learn nonlinear constitutive laws using physics-augmented Neural NetworksIACM Mechanistic Machine Learning and Digital Engineering for Computational Science Engineering and Technology., Sep 2023, El Paso, United States |
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Réseaux de neurones informés par la physique pour l’apprentissage de lois de comportementIA POUR LES SCIENCES DE L'INGÉNIERIE, Jun 2022, Online, France |
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Réseaux de neurones informés par la physique pour l’apprentissage de lois de comportement.25ème Congrès Français de Mécanique 2022, Aug 2022, Nantes, France |
Physics-informed neural networks derived from a mCRE functional for constitutive modellingIUTAM Symposium on Data-driven Mechanics, Oct 2022, Paris, France |
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Physics-informed neural networks derived from a mCRE functional for constitutive modellingArtificial Intelligence and Augmented Engineering, Dec 2022, Palaiseau, France |
Data-based Model Updating, Selection, and Enrichment using the Modified Constitutive Relation Error Concept15th World Congress on Computational Mechanics, Jul 2022, Yokohama, Japan |
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Scientific machine learning and physics-augmented neural networks for hybrid digital twinsJournée du GDR I-GAIA, 2023, Paris, France |
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Physics-augmented neural networks for constitutive modeling: training with the modified Constitutive Relation ErrorMORTech 2023 – 6th International Workshop on Model Reduction Techniques, Nov 2023, Gif-sur-Yvette, France |
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A novel DDDAS architecture combining advanced sensing and simulation technologies for effective real-time structural health monitoringErik Blasch, Frederica Darema, Alex Aved. Handbook of Dynamic Data Driven Applications Systems, 3 (chap. 10), Springer Cham, 2025, 978-3-031-88573-0. ⟨10.1007/978-3-031-88574-7_10⟩ |
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Apprentissage non-supervisé de lois de comportement nonlinéaires avec réseau de neurones thermodynamiquement consistent par minimisation de l'erreur en relation de comportement modifiée2024 |
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Use of physics-augmented neural networks for unsupervised learning of material constitutive relations - Comparison of the NN-Euclid and NN-mCRE methodsENS Paris-Saclay; Centrale Supélec. 2023 |