| Physics-informed deep-learning applications to experimental fluid mechanics H Eivazi, Y Wang, R Vinuesa Measurement science and technology 35 (7), 075303, 2024 | 57 | 2024 |
| β-Variational autoencoders and transformers for reduced-order modelling of fluid flows A Solera-Rico, C Sanmiguel Vila, M Gómez-López, Y Wang, A Almashjary, ... Nature Communications 15 (1), 1361, 2024 | 51 | 2024 |
| Towards optimal β-variational autoencoders combined with transformers for reduced-order modelling of turbulent flows Y Wang, A Solera-Rico, CS Vila, R Vinuesa International Journal of Heat and Fluid Flow 105, 109254, 2024 | 14 | 2024 |
| Easy attention: A simple self-attention mechanism for transformers M Sanchis-Agudo, Y Wang, K Duraisamy, R Vinuesa arXiv preprint arXiv:2308.12874, 2023 | 9 | 2023 |
| Opposition control applied to turbulent wings Y Wang, M Atzori, R Vinuesa arXiv preprint arXiv:2408.15588, 2024 | 2 | 2024 |
| A Deep-Learning Method Using Auto-encoder and Generative Adversarial Network for Anomaly Detection on Ancient Stone Stele Surfaces Y Liu, Y Wang, C Liu arXiv preprint arXiv:2308.04426, 2023 | 2 | 2023 |
| Convolution-compacted visiontransformers forprediction of localwall heat flux atmultiple Prandtlnumbers in turbulentchannel flow Y Wang | 1 | 2023 |
| Integration of Temporal Dynamics in Graph U-Nets for Improved Mesh-Agnostic Spatio-Temporal Flow Prediction S Yang, Y Wang, A Vishwasrao, R Vinuesa, N Kang Bulletin of the American Physical Society, 2024 | | 2024 |
Ricardo VinuesaAssociate Professor, KTH Royal Institute of Technology在 mech.kth.se 的电子邮件经过验证
