| Investigation of deep learning methods for efficient high-fidelity simulations in turbulent combustion KM Gitushi, R Ranade, T Echekki Combustion and Flame 236, 111814, 2022 | 25 | 2022 |
| Hydrogen gas dispersion studies for hydrogen fuel cell vessels II: Fuel cell room releases and the influence of ventilation KM Gitushi, ML Blaylock, LE Klebanoff International Journal of Hydrogen Energy 47 (50), 21492-21505, 2022 | 18 | 2022 |
| Generalized joint probability density function formulation inturbulent combustion using deeponet R Ranade, K Gitushi, T Echekki arXiv preprint arXiv:2104.01996, 2021 | 15 | 2021 |
| A Data-Based Hybrid Chemistry Acceleration Framework for the Low-Temperature Oxidation of Complex Fuels S Alqahtani, KM Gitushi, T Echekki Energies 17 (3), 734, 2024 | 1 | 2024 |
| Simulations for Planning of Liquid Hydrogen Spill Test K Mangala Gitushi, M Blaylock, ES Hecht Energies 16 (4), 1580, 2023 | 1 | 2023 |
| A PINN-DeepONet framework for extracting turbulent combustion closure from multiscalar measurements A Taassob, A Kumar, KM Gitushi, R Ranade, T Echekki Computer Methods in Applied Mechanics and Engineering 429, 117163, 2024 | | 2024 |
| Comparisons of Different Representative Species Selection Schemes for Reduced-Order Modeling and Chemistry Acceleration of Complex Hydrocarbon Fuels KM Gitushi, T Echekki Energies 17 (11), 2604, 2024 | | 2024 |
| Deep Learning of Joint Scalar PDFs in Turbulent Flames from Sparse Multiscalar Data R Ranade, KM Gitushi, T Echekki Combustion Science and Technology, 1-22, 2023 | | 2023 |
| A data-based hybrid chemistry acceleration framework for complex fuels oxidation at low temperatures S Alqahtani, KM Gitushi, T Echekki | | 2023 |
