• Serhani, A., Xing, V., Dupuy, D., Lapeyre, C., & Staffelbach, G. (2024). Graph and convolutional neural network coupling with a high-performance large-eddy simulation solver. Computers & Fluids, 106306.
  • Dupuy, D., Odier, N., & Lapeyre, C. (2024). Using graph neural networks for wall modeling in compressible anisothermal flows. Data-Centric Engineering, 5, e10.
  • Barrela, E., Berthet, P., Trani, M., Thual, O., & Lapeyre, C. (2023). Four-Dimensional History Matching Using ES-MDA and Flow-Based Distance-to-Front Measurement. Energies, 16(24), 7984.
  • Guiberti, T. F., Shohdy, N. N., Cardona, S., Zhu, X., Selle, L., & Lapeyre, C. J. (2023). Chemiluminescence-and machine learning-based monitoring of premixed ammonia-methane-air flames. Applications in Energy and Combustion Science, 16, 100212.
  • Drozda, L., Mohanamuraly, P., Cheng, L., Lapeyre, C., Daviller, G., Realpe, Y., Adler, A., Staffelback, G., Poinsot, T. (2023). Learning an optimised stable Taylor-Galerkin convection scheme based on a local spectral model for the numerical error dynamics. Journal of Computational Physics, accepted for publication.
  • Shin, J., Xing, V., Pfitzner, M., & Lapeyre, C. (2023). Probabilistic deep learning of turbulent premixed combustion. AIP Advances, 13(8). doi:10.1063/5.0146268
  • Coulon, V., Gaucherand, J., Xing, V., Laera, D., Lapeyre, C., & Poinsot, T. (2023). Direct numerical simulations of methane, ammonia-hydrogen and hydrogen turbulent premixed flames. Combustion and Flame, 256, 112933. doi:10.1016/j.combustflame.2023.112933
  • Dupuy, D., Odier, N., & Lapeyre, C. (2023). Data-driven wall modeling for turbulent separated flows. Journal of Computational Physics, 487, 112173.
  • Dupuy, D., Odier, N., Lapeyre, C., & Papadogiannis, D. (2023). Modeling the wall shear stress in large-eddy simulation using graph neural networks. Data-Centric Engineering, 4, e7. doi:10.1017/dce.2023.2
  • Lazzara, M., Chevalier, M., Colombo, M., Garay Garcia, J., Lapeyre, C., Teste, O. (2022). Surrogate modelling for an aircraft dynamic landing loads simulation using an LSTM AutoEncoder-based dimensionality reduction approach, Aerospace Science and Technology, 126, 107629, doi:10.1016/j.ast.2022.107629.
  • Yewgat, A., Busby, D., Chevalier, M. et al. (2022). Physics-constrained deep learning forecasting: an application with capacitance resistive model. Comput Geosci. doi:10.1007/s10596-022-10146-6
  • Besombes, C., Pannekoucke, O., Lapeyre, C., Sanderson, B. & Thual, O. (2021). Producing realistic climate data with GANs. Nonlinear Processes in Geophysics, 28, 347–370. doi:10.5194/npg-28-347-2021
  • Xing V., Lapeyre C., Jaravel T. & Poinsot T. (2021). Generalization Capability of Convolutional Neural Networks for Progress Variable Variance and Reaction Rate Subgrid-Scale Modeling. Energies 14(16):5096. doi:10.3390/en14165096
  • A. Cellier, C.J. Lapeyre, G. Öztarlik, T. Poinsot, T. Schuller, & L. Selle (2021). Detection of precursors of combustion instability using convolutional recurrent neural networks. Combustion and Flame, Volume 233, 111558. doi:10.1016/j.combustflame.2021.111558
  • Lapeyre, C. J., Cazard, N., Roy, P. T., Ricci, S., & Zaoui, F. (2020). Reconstruction of Hydraulic Data by Machine Learning. In Advances in Hydroinformatics (pp. 701-715). Springer, Singapore. doi:10.1007/978-981-15-5436-0_54
  • Lapeyre, C.J., Misdariis, A., Cazard, N., Veynante, D. & Poinsot, T. (2019). Training convolutional neural networks to estimate turbulent sub-grid scale reaction rates. Combustion and Flame, 203, 255-264. doi:10.1016/j.combustflame.2019.02.019
  • Lapeyre, C.J., Misdariis, A., Cazard, N. & Poinsot, T (2018). A-posteriori evaluation of a deep convolutional neural network approach to subgrid-scale flame surface estimation. Proc. CTR Summer Program, 349-358. PDF
  • Thiesset, F., Halter, F., Bariki, C.e, Lapeyre, C., Chauveau, C., Gokalp, I., Selle, L. & Poinsot, T. (2017). Isolating strain and curvature effects in premixed flame/vortex interactions. Journal of Fluid Mechanics, 831, 618-654. doi:10.1017/jfm.2017.641
  • Huet, M., Vuillot, F., Bertier, N., Mazur, M., Kings, N., Tao, W., Scouflaire, P., Richecoeur, F., Ducruix, S., Lapeyre, C. & Poinsot, T. (June 2016). Recent improvements in combustion noise investigation: from combustion chamber to nozzle flow. Aerospace Lab, (11), 10. doi:10.12762/2016.AL11-10
  • Lapeyre, C. J., Mazur, M., Scouflaire, P., Richecoeur, F., Ducruix, S., & Poinsot, T. (2017). Acoustically induced flashback in a staged swirl-stabilized combustor. Flow, Turbulence and Combustion, 98(1), 265-282. doi:10.1007/s10494-016-9745-2


  • Defontaine, T., Ricci, S., Lapeyre, C. J., Marchandise, A., & Le Pape, E. (2024). Real-time flood forecasting with Machine Learning using scarce rainfall-runoff data. EGUsphere, 2024, 1-32.
  • Barrela, E., Berthet, P., Trani, M., Thual, O., & Lapeyre, C. (2023, June) 4D history matching using ESMDA and flow-based distance-to-front measurement. In 84th EAGE Annual Conference & Exhibition (Vol. 2023, No. 1, pp. 1-5). European Association of Geoscientists & Engineers.
  • Defontaine, T., Ricci, S., Lapeyre, C., Marchandise, A., & Le Pape, E. (2023). Flood forecasting with Machine Learning in a scarce data layout. In IOP Conference Series: Earth and Environmental Science (Vol. 1136, No. 1, p. 012020). IOP Publishing. doi:10.1088/1755-1315/1136/1/012020
  • Serhani, A., Xing, V., Dupuy, D., Lapeyre, C., Staffelbach, G. (2022). High-performance hybrid coupling of a CFD solver to deep neural networks. 33rd Parallel CFD International Conference, May 25-27, Alba, Italy.
  • ElMontassir, R., Lapeyre, C., Pannekoucke, O. (2022). Hybrid Physics-AI Approach for Cloud Cover Nowcasting. ECMWF Machine Learning Workshop.
  • Drozda, L., Mohanamuraly, P., Realpe, Y., Lapeyre, C., Adler, A., Daviller, G., & Poinsot, T. (2021). Data-driven Taylor-Galerkin finite-element scheme for convection problems. The Symbiosis of Deep Learning and Differential Equations - Neurips 2021 Workshop
  • Badhe, Abhijeet, Laurent, Charlelie, Lapeyre, Corentin, & Nicoud, Franck. (2021, September 10). Low-Order Thermoacoustic Analysis of Real Engines. Symposium on Thermoacoustics in Combustion: Industry meets Academia (SoTiC 2021), Munich, Germany. doi:10.5281/zenodo.6394819
  • Yewgat, A., Busby, D., Chevalier, M., Lapeyre, C., & Teste, O. (2020, September). Deep-CRM: A New Deep Learning Approach For Capacitance Resistive Models. ECMOR XVII (Vol. 2020, No. 1, pp. 1-19). European Association of Geoscientists & Engineers.
  • Paugam, R., Rochoux, M., Cazard, M., Lapeyre, C., Mell, W., Johnston, J., and Wooster, M. (2019). Computing High Resolution Fire Behavior Metrics from Prescribed Burn using Handheld Airborne Thermal Camera Observations. The 6th International Fire Behaviour and Fuels Conference, Marseille, May 2019.
  • Lapeyre, C. J., Cazard, N., Roy, P. T., Ricci, S., & Zaoui, F. (2019). Reconstruction of Hydraulic Data by Machine Learning. SimHydro 2019, Nice, France, June 12-14.
  • Lapeyre, C.J., Misdariis, A., Cazard, N., Xing, V., Veynante, D. & Poinsot, T. (2019). A convolutional neural network-based efficiency function for sub-grid flame-turbulence interaction in LES. 17th International Conference on Numerical Combustion, May 6-8 2019, Aachen Germany.
  • Lapeyre, C.J., Misdariis, A., Cazard, N, Poinsot, T. (2018). Replacing a sub-grid closure model with a trained deep convolutional neural network. HiFiLeD Symposium, November 14-16th 2018, Brussels Belgium. Presentation
  • Lapeyre, C.J., Mazur, M., Scouflaire, P., Richecoeur, F, Ducruix, S., Poinsot, T. (2015). Acoustically induced vortex core flashback in a staged swirl-stabilized combustor. 15th International Conference on Numerical Combustion, April 20-22nd 2015, Avignon France. Presentation
  • Lapeyre, C.J., Poinsot, T. (2013). Importance of thermoacoustics in LES of combustion noise in realistic confined configurations. MAMBO Workshop, October 18th 2013, ONERA Châtillon France. Presentation

Book Chapters

  • Xing, V., and C. J. Lapeyre. “Deep Convolutional Neural Networks for Subgrid-Scale Flame Wrinkling Modeling.” Machine Learning and Its Application to Reacting Flows: ML and Combustion. Cham: Springer International Publishing, 2023. 149-174.

Technical Reports


Below is a summary of the students I currently advise closely, and those I advised in the past.

Current Students

  • Mercier, Valentin, PhD Student. Physical constraints for deep learning of time-evolution of Barré-Saint Venant solutions
  • El Montassir, Rachid, PhD Student. Hybrid data-driven and physics-informed techniques for nebulosity nowcasting
  • Alas, Rémi, PhD Student. Data-driven wall models for underresolved features in atmospheric simulations
  • Coulon, Victor, PhD Student. Data-driven turbulent combustion models for hydrogen-based aircraft engines

Past Students


  • Defontaine, Théo, PhD Student. Heterogeneous data aggregation for flood forecasting
  • Drozda, Luciano, Postdoctoral Fellow. Locally adaptative unstructured numerical schemes for high precision and robustness to mesh irregularities





  • Chayti, El Mahdi, PhD Student. Fusion de données pour l’estimation de modèles aérodynamiques en utilisant une approche bayésienne et de l’apprentissage machine


  • Cazard, Nicolas, Data Scientist. Various projects
  • Cellier, Antony, Masters internship. Detection and Identification of Instability and Blow-off/Flashback Precursors in Aeronautical Engines using Deep Learning techniques
  • Labarrère, Laure, Postdoctoral Fellow. Neural networks inside a large scale fluid solver
  • Nony, Bastien, Masters internship. Learning techniques for missing data reconstruction and prediction in hydraulics
  • Patil, Aakash, Masters internship. Deep learning for turbulence generation


  • Cazard, Nicolas. Feb - Jul (Masters internship), Deep learning applied to turbulent combustion modelling
  • Ronzie, Maxence. Feb - Jul (Masters internship), HPC aspects of Deep Learning. Application to satellite data
  • Besombes, Camille. Apr - Sep (Masters internship), Generative neural networks applied to reservoir modeling
  • Xing, Victor. June - Nov (Masters internship), Neural networks for compression of turbulent fields