Publications

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. doi.org:10.1016/j.jcp.2023.112173
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

Conferences

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

Lapeyre, C.J., Selle, L., Bedat, B., Poinsot, T. (2016). ANR IDYLLE Deliverables Report

Students

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

2024

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

2023

Yewgat, Abderrahemane, Ph.D Student (dropped-out). Physics-informed deep learning for fast reservoir forecasting
Mekonou, Lydie, Masters Intern. Predicting extreme rainfalls in west africa using machine learning
Mrini, Soufiane, Masters Intern. Diffusion-based 3D turbulent generation
Lazzara, Michele, PhD Student (defended 2023). Apprentissage Profond pour la Modélisation de Systèmes Dynamiques Non-linéaires de Haute Dimension avec Traitement de Séries Temporelles Multivariées
Serhani, Anass, Postdoctoral Fellow. Introducing deep learning inside a high-performance massively parallel turbulent combustion solver - at scale

2022

Dupuy, Dorian, Postdoctoral Fellow. Graph neural networks for turbulence wall models in LES
Cheng, Lionel, Postdoctoral Fellow. High-order unstructured numerical schemes guided by graph networks
Xing, Victor, PhD Student (defended 2022). Deep learning for subgrid-scale modeling in large eddy simulations of turbulent premixed combustion

2021

Besombes, Camille, PhD Student (defended 2021). Parameterization using Generative Adversarial Networks for Control Space Reduction in Data Assimilation.
Gullaud, Elsa, Postdoctoral Fellow. Detecting endagered predatory fauna to protect herds by targeted scaring techniques

2020

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

2019

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

2018

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