| 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 | 3 | 2024 |
| Physics-informed neural networks for turbulent combustion: Toward extracting more statistics and closure from point multiscalar measurements A Taassob, R Ranade, T Echekki Energy & Fuels 37 (22), 17484-17498, 2023 | 3 | 2023 |
| Derived scalar statistics from multiscalar measurements via surrogate composition spaces A Taassob, T Echekki Combustion and Flame 250, 112641, 2023 | 3 | 2023 |
| Neural deep operator networks representation of coherent ising machine dynamics A Taassob, D Venturelli, PA Lott Machine Learning with New Compute Paradigms, 2023 | 3 | 2023 |
| Turbulent Combustion Closure via Physic-Informed Neural Networks and Multiscalar Measurements A Taassob, R Ranade, T Echekki 13th U.S. National Combustion Meeting, 2023 | 1 | 2023 |
| A Robust Turbulent Combustion Closure model via Deep Operator Network A Taassob, A Kumar, T Echekki, R Ranade Bulletin of the American Physical Society, 2023 | | 2023 |