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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
Book sections
hal-03042541v1
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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
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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
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Machine learning for sub-grid scale turbulent combustion modeling
Andréa Seltz
,
Pascale Domingo
,
Luc Vervisch
9th ECM, 2019, Lisbonne, Portugal
Conference papers
hal-02132220v1
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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
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
Journal articles
hal-03116162v1
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