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authIdHal_s : andrea-seltz
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From discrete and iterative deconvolution operators to machine learning for premixed turbulent combustion modeling.

Pascale Domingo , Zacharias Nikolaou , Andréa Seltz , Luc Vervisch
Data analysis for direct numerical simulation of turbulent combustion, 2020, ⟨10.1007/978-3-030-44718-2_11⟩
Book sections hal-03042541v1

Machine learning for sub-grid scale turbulent combustion modeling

Andréa Seltz , Pascale Domingo , Luc Vervisch
15th US National Congress on Computational Mechanics, 2019, Austin, United States
Conference papers hal-02420284v1

Machine learning for turbulent combustion modeling in high-fidelity LES

Andréa Seltz , Pascale Domingo , Luc Vervisch
1rst HiFiLeD Symposium, 2018, Bruxelles, Belgium
Conference papers hal-02420297v1

Machine learning for sub-grid scale turbulent combustion modeling

Andréa Seltz , Pascale Domingo , Luc Vervisch
9th ECM, 2019, Lisbonne, Portugal
Conference papers hal-02132220v1

Large-eddy simulation of premixed turbulent combustion using a convolutional neural network

Andréa Seltz , Pascale Domingo , Luc Vervisch
SIAM Int. Conf. on Numerical Combustion, 2019, Aix-la-Chapelle, Germany
Conference papers hal-02132225v1
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Direct mapping from LES resolved scales to filtered-flame generated manifolds using convolutional neural networks

Andréa Seltz , Pascale Domingo , Luc Vervisch , Zacharias Nikolaou
Combustion and Flame, 2019, 210, pp.71-82. ⟨10.1016/j.combustflame.2019.08.014⟩
Journal articles hal-02313873v1
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Solving the population balance equation for non-inertial particles dynamics using probability density function and neural networks: Application to a sooting flame

Andréa Seltz , Pascale Domingo , Luc Vervisch
Physics of Fluids, 2021, 33 (1), pp.013311. ⟨10.1063/5.0031144⟩
Journal articles hal-03116162v1