| Deep reinforcement learning for flow control exploits different physics for increasing Reynolds number regimes P Varela, P Suárez, F Alcántara-Ávila, A Miró, J Rabault, B Font, ... Actuators 11 (12), 359, 2022 | 32 | 2022 |
| Deep learning of the spanwise-averaged Navier–Stokes equations B Font, GD Weymouth, VT Nguyen, OR Tutty Journal of Computational Physics 434, 110199, 2021 | 30 | 2021 |
| Span effect on the turbulence nature of flow past a circular cylinder B Font, GD Weymouth, VT Nguyen, OR Tutty Journal of Fluid Mechanics 878, 306-323, 2019 | 19* | 2019 |
| A data-driven wall-shear stress model for LES using gradient boosted decision trees S Radhakrishnan, LA Gyamfi, A Miró, B Font, J Calafell, O Lehmkuhl International Conference on High Performance Computing, 105-121, 2021 | 11 | 2021 |
| Active flow control for three-dimensional cylinders through deep reinforcement learning P Suárez, F Alcántara-Ávila, A Miró, J Rabault, B Font, O Lehmkuhl, ... arXiv preprint arXiv:2309.02462, 2023 | 6 | 2023 |
| Active flow control of a turbulent separation bubble through deep reinforcement learning B Font, F Alcántara-Ávila, J Rabault, R Vinuesa, O Lehmkuhl Journal of Physics: Conference Series 2753 (1), 012022, 2024 | 2 | 2024 |
| WaterLily. jl: A differentiable fluid simulator in Julia with fast heterogeneous execution GD Weymouth, B Font arXiv preprint arXiv:2304.08159, 2023 | 2 | 2023 |
| Turbulent wake prediction using deep convolutional neural networks B Font, G Weymouth, VT Nguyen, O Tutty | 1 | 2020 |
| Analysis of two-dimensional and three-dimensional wakes of long circular cylinders B Font, GD Weymouth, OR Tutty OCEANS 2017-Aberdeen, 1-8, 2017 | 1 | 2017 |
| Deep reinforcement learning for active flow control in a turbulent separation bubble B Font, F Alcántara-Ávila, J Rabault, R Vinuesa, O Lehmkuhl | | 2024 |
| Active flow control for drag reduction through multi-agent reinforcement learning on a turbulent cylinder at P Suárez, F Alcantara-Avila, A Miró, J Rabault, B Font, O Lehmkuhl, ... arXiv preprint arXiv:2405.17655, 2024 | | 2024 |
| Flow control of three-dimensional cylinders transitioning to turbulence via multi-agent reinforcement learning P Suárez, F Álcantara-Ávila, J Rabault, A Miró, B Font, O Lehmkuhl, ... arXiv preprint arXiv:2405.17210, 2024 | | 2024 |
| WaterLily: A fast differentiable CPU/GPU flow simulator in Julia G Weymouth, B Font Bulletin of the American Physical Society, 2023 | | 2023 |
| The effect of physical constraints on the loss function landscapes of deep learning models M Cabral, B Font, G Weymouth Bulletin of the American Physical Society, 2023 | | 2023 |
| Deep reinforcement learning for active separation control in a turbulent boundary layer F Alcántara-Ávila, B Font, J Rabault, R Vinuesa, O Lehmkuhl Bulletin of the American Physical Society, 2023 | | 2023 |
| On the entropy-viscosity method for flux reconstruction B Font, A Miró, O Lehmkuhl 2nd Spanish Fluid Mechanics Conference, 2023 | | 2023 |
| Modelling of flow past long cylindrical structures B Font arXiv preprint arXiv:2012.07845, 2020 | | 2020 |
| Deep learning the spanwise-averaged turbulent wake of a circular cylinder B Font, G Weymouth, VT Nguyen, O Tutty Bulletin of the American Physical Society 64, 2019 | | 2019 |
| High-order shock-capturing schemes for micro shock tubes B Font, L Könözsy | | 2015 |
Gabriel WeymouthShip Hydromechanics, TU Delft在 tudelft.nl 的电子邮件经过验证
