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Références bibliographiques
72
Dépôts avec texte intégral
140
Twitter @LisnLab
LAst publications
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Jai Kumar, Anne Sergent, Francesca Chillà, Julien Salort, Didier Lucor. Bridging Experimental shadowgraphs and DNS in Turbulent Convection Using physically-informed U-Net. Joint event Euromech Colloquium on Data-Driven Fluid Dynamics/2nd ERCOFTAC Workshop on Machine Learning for Fluid Dynamics, Apr 2025, London, United Kingdom. ⟨hal-04924440⟩
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Soufiane Mrini, Anne Sergent, Francesca Chillà, Julien Salort, Didier Lucor. Addressing turbulent convection experimental data challenges in PINNs with appropriate physical sampling. Joint event Euromech Colloquium on Data-Driven Fluid Dynamics/2nd ERCOFTAC Workshop on Machine Learning for Fluid Dynamics, Apr 2025, London, United Kingdom. ⟨hal-04924450⟩
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Marie-Christine Volk, Didier Lucor, Anne Sergent, Michael Mommert, Christian Bauer, et al.. A PINN Methodology for Temperature Field Inference in the PIV Measurement Plane: Case of Rayleigh-Bénard Convection. Joint event Euromech Colloquium on Data-Driven Fluid Dynamics/2nd ERCOFTAC Workshop on Machine Learning for Fluid Dynamics, Apr 2025, London, United Kingdom. ⟨hal-04924426⟩
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Ikroh Yoon, Seungwon Shin, Damir Juric, Jalel Chergui. Numerical investigation of spreading time in droplet impact with spherical surfaces: from physical analysis to data-driven prediction model. Theoretical and Computational Fluid Dynamics, 2024, 38 (2), pp.225-250. ⟨10.1007/s00162-024-00698-x⟩. ⟨hal-04647253⟩
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Fuyue Liang, Juan Valdes, Sibo Cheng, Lyes Kahouadji, Seungwon Shin, et al.. Liquid–Liquid Dispersion Performance Prediction and Uncertainty Quantification Using Recurrent Neural Networks. Industrial and engineering chemistry research, 2024, 63 (17), pp.7853-7875. ⟨10.1021/acs.iecr.4c00014⟩. ⟨hal-04647255⟩