Full professor
Head of the Mechanics-Energetic department
Co-head of Master of Mechanics of Paris-Saclay Univ.
Fluid Mechanics - Energetics
Fluid Mechanics – Energetics
Approach and perspective
The approach of the Mechanics-Energetics department is at the interface between computer science, physics and applied mathematics.
We wish to maintain a balance between interdependent activities:
- understanding the fundamental phenomena of turbulent fluid mechanics,
- tackling complex multiphysics problems coupled at large scales,
- leveraging our physical knowledge while considering data as an inherent part of modeling, experiments and simulations.
In this context, we are very open to recent developments in machine learning, which offer a powerful information processing framework that can augment our current lines of research with broad-spectrum applications in the energy, transportation, health, and environmental sectors.
Organization
The Mechanics-Energetics Department offers original and multidisciplinary research thanks to the expertise of some twenty permanent staff, researchers, teacher-researchers and engineers, organized into two complementary teams: DATAFLOT (DAta science, TrAnsition, FLuid instabiLity, contrOl & Turbulence) relying on data-augmented modeling and artificial intelligence, and studying fluid dynamics, instabilities and turbulence, and COMET (COuplages MultiphysiquEs et Transferts) focusing on the understanding of complex coupled fluid phenomena, involved in energy conversion and storage, heat transfers as well as energy efficiency optimization.
Coordination
News
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Highlight, Interaction with the Human
Moisson de prix pour AViZ
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Climate, Colloquium
Utopia or Dystopia: Perspectives & choices in ICT
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ARAIAugmented Reality and Artificial Intelligence, Highlight, Interaction with the Human, VENISE
Christian Sandor gets a Best XR Demo at ACM SIGGRAPH Asia
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Schedule
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From
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Interactions with Human, Thesis
Enhancing Human Performance Through Augmented Vision
Location LISN Site Belvédère
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From
Time –
Interactions with Human, Thesis
Generation of Facial Nonverbal Behavior for Socially Interactive Agents: A Convolutional Generative Adversarial Approach
Location Amphithéâtre Hexagone, Campus Universitaire de Luminy 1309 Marseille
Speaker : Alice Delbosc
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From to
Last publications
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Communication dans un congrès
Zatout Yanis, Semeraro Onofrio, Mathelin Lionel, Bataille Françoise, Adrien Toutant. Fast and accurate field reconstruction of Thermal-Large Eddy Simulation (T-LES) by Deep Learning. 31ème Congrès Français de Thermique, Institut de Thermique, Mécanique, Matériaux, May 2023, Reims, France. ⟨hal-05454994⟩
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Article dans une revue
Abdullah Abdal, Debashis Panda, Lyes Kahouadji, Mosayeb Shams, Seungwon Shin, et al.. Three-dimensional numerical simulations of product changeover: miscible and immiscible displacements in circular tubes. International Journal of Multiphase Flow, 2026, 197, pp.105634. ⟨10.1016/j.ijmultiphaseflow.2026.105634⟩. ⟨hal-05490276⟩
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Article dans une revue
Andrea Düll, Alexander Nies, Álvaro Echeverría de Encio, Lyes Kahouadji, Seungwon Shin, et al.. Three-dimensional effects on carbon capture in wavy falling films. International Journal of Multiphase Flow, 2026, 197, pp.105624. ⟨10.1016/j.ijmultiphaseflow.2026.105624⟩. ⟨hal-05490273⟩
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Article dans une revue
Yanis Zatout, Adrien Toutant, Onofrio Semeraro, Lionel Mathelin, Françoise Bataille. A priori reconstruction of Thermal-Large Eddy Simulation (T-LES) by Deep Learning Reconstruction a priori de champs de Simulations des Grandes Echelles Thermiques par Apprentissage Profond. Entropie : thermodynamique – énergie – environnement – économie, 2023, 4 (3), ⟨10.21494/ISTE.OP.2023.1015⟩. ⟨hal-05454757⟩
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Poster de conférence
Yanis Zatout, Adrien Toutant, Onofrio Semeraro, Lionel Mathelin, Françoise Bataille. Weakly supervised learning for a priori reconstruction of Thermal Large Eddy Simulations using two-point correlations Modeling of the solar receiver Methods Context Context Modeling and methods Modeling and methods Results and conclusion Results and conclusion. Congrès Français de Thermique, Jun 2024, Strasbourg, France. ⟨hal-05454986⟩
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Thèse
Vincent Blot. Conformal predictions and risk control in machine learning models to improve performance and human decision-making. Computer Vision and Pattern Recognition [cs.CV]. Université Paris-Saclay, 2025. English. ⟨NNT : 2025UPASG102⟩. ⟨tel-05448293⟩
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Communication dans un congrès
Luc Lebon, Paul Boniface, Chi-Tuong Pham, Laurent Limat. Allée de vortex de Bénard-von Kármán confinée : selection de longueur d’onde par les instabilités de kelvin-Helmholtz. 28e Rencontre du Non Linéaire, Mar 2025, Paris, France. ⟨hal-05412081⟩
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Poster de conférence
Luc Lebon, Paul Boniface, Chi-Tuong Pham, Laurent Limat. Allée de vortex de Bénard-von Kármán confinée : sélection de longueur d’onde par les instabilités de kelvin-Helmholtz. Rencontre du Non Linéaire 2025 (RNL 2025), Mar 2025, Paris, France. . ⟨hal-05412053⟩
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Article dans une revue
Rémi Bousquet, Caroline Nore, Didier Lucor. AutoEncoders latent space interpretability in the light of proper orthogonal decomposition: Machine learning of periodically forced fluid flows. Computer Physics Communications, 2025, 315, pp.109728. ⟨10.1016/j.cpc.2025.109728⟩. ⟨hal-05407939⟩
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Pré-publication, Document de travail
Luigi Marra, Onofrio Semeraro, Lionel Mathelin, Andrea Meilán-Vila, Stefano Discetti. Latent-Space Non-Linear Model Predictive Control for Partially-Observable Systems. 2025. ⟨hal-05394151⟩