| Deep learning methods for Reynolds-averaged Navier–Stokes simulations of airfoil flows N Thuerey, K Weißenow, L Prantl, X Hu AIAA Journal 58 (1), 25-36, 2020 | 566 | 2020 |
| Lagrangian fluid simulation with continuous convolutions B Ummenhofer, L Prantl, N Thuerey, V Koltun International Conference on Learning Representations, 2019 | 215 | 2019 |
| Generating liquid simulations with deformation-aware neural networks L Prantl, B Bonev, N Thuerey ICLR, 2017 | 32* | 2017 |
| Guaranteed conservation of momentum for learning particle-based fluid dynamics L Prantl, B Ummenhofer, V Koltun, N Thuerey Advances in Neural Information Processing Systems 35, 6901-6913, 2022 | 30 | 2022 |
| Tranquil Clouds: Neural Networks for Learning Temporally Coherent Features in Point Clouds L Prantl, N Chentanez, S Jeschke, N Thuerey ICLR, 2020 | 19 | 2020 |
| Physics-based deep learning for fluid flow N Thuerey, Y Xie, M Chu, S Wiewel, L Prantl see https://www. semanticscholar. org/paper/Physics-Based-Deep-Learning-for …, 0 | 1 | |
| Wavelet-based Loss for High-frequency Interface Dynamics L Prantl, J Bender, T Kugelstadt, N Thuerey arXiv preprint arXiv:2209.02316, 2022 | | 2022 |
