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Lukas Prantl
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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
4372020
Lagrangian fluid simulation with continuous convolutions
B Ummenhofer, L Prantl, N Thuerey, V Koltun
International Conference on Learning Representations, 2019
1792019
Generating liquid simulations with deformation-aware neural networks
L Prantl, B Bonev, N Thuerey
ICLR, 2017
31*2017
Tranquil Clouds: Neural Networks for Learning Temporally Coherent Features in Point Clouds
L Prantl, N Chentanez, S Jeschke, N Thuerey
ICLR, 2020
182020
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
172022
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
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