| Training convolutional neural networks to estimate turbulent sub-grid scale reaction rates CJ Lapeyre, A Misdariis, N Cazard, D Veynante, T Poinsot Combustion and Flame 203, 255-264, 2019 | 170 | 2019 |
| Surrogate modelling for an aircraft dynamic landing loads simulation using an LSTM AutoEncoder-based dimensionality reduction approach M Lazzara, M Chevalier, M Colombo, JG Garcia, C Lapeyre, O Teste Aerospace Science and Technology 126, 107629, 2022 | 31 | 2022 |
| Detection of precursors of combustion instability using convolutional recurrent neural networks A Cellier, CJ Lapeyre, G Öztarlik, T Poinsot, T Schuller, L Selle Combustion and Flame 233, 111558, 2021 | 24 | 2021 |
| Isolating strain and curvature effects in premixed flame/vortex interactions F Thiesset, F Halter, C Bariki, C Lapeyre, C Chauveau, I Gökalp, L Selle, ... Journal of Fluid Mechanics 831, 618-654, 2017 | 23 | 2017 |
| Recent improvements in combustion noise investigation: from the combustion chamber to nozzle flow M Huet, F Vuillot, N Bertier, T Poinsot, M Mazur, N Kings, W Tao, ... Aerospace Lab, 10, 2016 | 20 | 2016 |
| Producing realistic climate data with generative adversarial networks C Besombes, O Pannekoucke, C Lapeyre, B Sanderson, O Thual Nonlinear Processes in Geophysics 28 (3), 347-370, 2021 | 18 | 2021 |
| Acoustically induced flashback in a staged swirl-stabilized combustor CJ Lapeyre, M Mazur, P Scouflaire, F Richecoeur, S Ducruix, T Poinsot Flow, Turbulence and Combustion 98, 265-282, 2017 | 17 | 2017 |
| Generalization capability of convolutional neural networks for progress variable variance and reaction rate subgrid-scale modeling V Xing, C Lapeyre, T Jaravel, T Poinsot Energies 14 (16), 5096, 2021 | 10 | 2021 |
| Deep-CRM: A new deep learning approach for capacitance resistive models A Yewgat, D Busby, M Chevalier, C Lapeyre, O Teste ECMOR XVII 2020 (1), 1-19, 2020 | 8 | 2020 |
| Direct numerical simulations of methane, ammonia-hydrogen and hydrogen turbulent premixed flames V Coulon, J Gaucherand, V Xing, D Laera, C Lapeyre, T Poinsot Combustion and Flame 256, 112933, 2023 | 6 | 2023 |
| Data-driven wall modeling for turbulent separated flows D Dupuy, N Odier, C Lapeyre Journal of Computational Physics 487, 112173, 2023 | 6 | 2023 |
| Reconstruction of hydraulic data by machine learning CJ Lapeyre, N Cazard, PT Roy, S Ricci, F Zaoui Advances in Hydroinformatics: SimHydro 2019-Models for Extreme Situations …, 2020 | 5 | 2020 |
| A-posteriori evaluation of a deep convolutional neural network approach to subgrid-scale flame surface estimation CJ Lapeyre, A Misdariis, N Cazard, T Poinsot Proceedings of the summer program, center for turbulence research, 349-358, 2018 | 4 | 2018 |
| Numerical study of flame stability, stabilization and noise in a swirl-stabilized combustor under choked conditions CJ Lapeyre Institut National Polytechnique de Toulouse-INPT, 2015 | 4 | 2015 |
| Modeling the wall shear stress in large-eddy simulation using graph neural networks D Dupuy, N Odier, C Lapeyre, D Papadogiannis Data-Centric Engineering 4, e7, 2023 | 3 | 2023 |
| Learning an optimised stable Taylor-Galerkin convection scheme based on a local spectral model for the numerical error dynamics L Drozda, P Mohanamuraly, L Cheng, C Lapeyre, G Daviller, Y Realpe, ... Journal of Computational Physics 493, 112430, 2023 | 2 | 2023 |
| Experimental assessment of the displacement and consumption speeds in flame/vortex interactions F Thiesset, F Halter, C Bariki, CJ Lapeyre, C Chauveau, I Gökalp, L Selle, ... International colloquium on the dynamics of explosions and reactive systems, 2017 | 2 | 2017 |
| Performance Study of Convolutional Neural Network Architectures for 3D Incompressible Flow Simulations E Ajuria Illarramendi, M Bauerheim, N Ashton, C Lapeyre, B Cuenot Proceedings of the Platform for Advanced Scientific Computing Conference, 1-11, 2023 | 1 | 2023 |
| Flood forecasting with Machine Learning in a scarce data layout T Defontaine, S Ricci, C Lapeyre, A Marchandise, E Le Pape IOP Conference Series: Earth and Environmental Science 1136 (1), 012020, 2023 | 1 | 2023 |
| Producing realistic climate data with GANs C Besombes, O Pannekoucke, C Lapeyre, B Sanderson, O Thual Zenodo [data set] 10, 0 | 1 | |
Thierry PoinsotCNRSVerified email at cerfacs.fr
