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7

Andréa Seltz


Journal articles2 documents

  • Andréa Seltz, Pascale Domingo, Luc Vervisch. Solving the population balance equation for non-inertial particles dynamics using probability density function and neural networks: Application to a sooting flame. Physics of Fluids, American Institute of Physics, 2021, 33 (1), pp.013311. ⟨10.1063/5.0031144⟩. ⟨hal-03116162⟩
  • Andréa Seltz, Pascale Domingo, Luc Vervisch, Zacharias Nikolaou. Direct mapping from LES resolved scales to filtered-flame generated manifolds using convolutional neural networks. Combustion and Flame, Elsevier, 2019, 210, pp.71-82. ⟨10.1016/j.combustflame.2019.08.014⟩. ⟨hal-02313873⟩

Conference papers4 documents

  • Andréa Seltz, Pascale Domingo, Luc Vervisch. Machine learning for sub-grid scale turbulent combustion modeling. 9th ECM, 2019, Lisbonne, Portugal. ⟨hal-02132220⟩
  • Andréa Seltz, Pascale Domingo, Luc Vervisch. Large-eddy simulation of premixed turbulent combustion using a convolutional neural network. SIAM Int. Conf. on Numerical Combustion, 2019, Aix-la-Chapelle, Germany. ⟨hal-02132225⟩
  • Andréa Seltz, Pascale Domingo, Luc Vervisch. Machine learning for sub-grid scale turbulent combustion modeling. 15th US National Congress on Computational Mechanics, 2019, Austin, United States. ⟨hal-02420284⟩
  • Andréa Seltz, Pascale Domingo, Luc Vervisch. Machine learning for turbulent combustion modeling in high-fidelity LES. 1rst HiFiLeD Symposium, 2018, Bruxelles, Belgium. ⟨hal-02420297⟩

Book sections1 document

  • Pascale Domingo, Zacharias Nikolaou, Andréa Seltz, Luc Vervisch. From discrete and iterative deconvolution operators to machine learning for premixed turbulent combustion modeling.. Data analysis for direct numerical simulation of turbulent combustion, 2020. ⟨hal-03042541⟩