| 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 | 523 | 2020 |
| Deep fluids: A generative network for parameterized fluid simulations B Kim, VC Azevedo, N Thuerey, T Kim, M Gross, B Solenthaler Computer graphics forum 38 (2), 59-70, 2019 | 493 | 2019 |
| tempogan: A temporally coherent, volumetric gan for super-resolution fluid flow Y Xie, E Franz, M Chu, N Thuerey ACM Transactions on Graphics (TOG) 37 (4), 1-15, 2018 | 468 | 2018 |
| WeatherBench: a benchmark data set for data‐driven weather forecasting S Rasp, PD Dueben, S Scher, JA Weyn, S Mouatadid, N Thuerey Journal of Advances in Modeling Earth Systems 12 (11), e2020MS002203, 2020 | 467 | 2020 |
| Lattice Boltzmann model for free surface flow for modeling foaming C Körner, M Thies, T Hofmann, N Thürey, U Rüde Journal of statistical Physics 121, 179-196, 2005 | 372 | 2005 |
| Latent space physics: Towards learning the temporal evolution of fluid flow S Wiewel, M Becher, N Thuerey Computer graphics forum 38 (2), 71-82, 2019 | 331 | 2019 |
| Wavelet turbulence for fluid simulation T Kim, N Thürey, D James, M Gross ACM Transactions on Graphics (TOG) 27 (3), 1-6, 2008 | 327 | 2008 |
| Learning temporal coherence via self-supervision for GAN-based video generation M Chu, Y Xie, J Mayer, L Leal-Taixé, N Thuerey ACM Transactions on Graphics (TOG) 39 (4), 75: 1-75: 13, 2020 | 294* | 2020 |
| Solver-in-the-loop: Learning from differentiable physics to interact with iterative pde-solvers K Um, R Brand, YR Fei, P Holl, N Thuerey Advances in Neural Information Processing Systems 33, 6111-6122, 2020 | 269 | 2020 |
| Neural scene graphs for dynamic scenes J Ost, F Mannan, N Thuerey, J Knodt, F Heide Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 263 | 2021 |
| Learning to control pdes with differentiable physics P Holl, V Koltun, N Thuerey arXiv preprint arXiv:2001.07457, 2020 | 214 | 2020 |
| Data-driven synthesis of smoke flows with CNN-based feature descriptors M Chu, N Thuerey ACM Transactions on Graphics (TOG) 36 (4), 1-14, 2017 | 210 | 2017 |
| Data‐driven medium‐range weather prediction with a resnet pretrained on climate simulations: A new model for weatherbench S Rasp, N Thuerey Journal of Advances in Modeling Earth Systems 13 (2), e2020MS002405, 2021 | 207 | 2021 |
| Lagrangian fluid simulation with continuous convolutions B Ummenhofer, L Prantl, N Thuerey, V Koltun International Conference on Learning Representations, 2019 | 205 | 2019 |
| Deforming meshes that split and merge C Wojtan, N Thürey, M Gross, G Turk ACM SIGGRAPH 2009 papers, 1-10, 2009 | 189 | 2009 |
| Detail-preserving fluid control N Thürey, R Keiser, M Pauly, U Rüde Graphical Models 71 (6), 221-228, 2009 | 178 | 2009 |
| Highly adaptive liquid simulations on tetrahedral meshes R Ando, N Thürey, C Wojtan ACM Transactions on Graphics (TOG) 32 (4), 1-10, 2013 | 153 | 2013 |
| A multiscale approach to mesh-based surface tension flows N Thürey, C Wojtan, M Gross, G Turk ACM Transactions on Graphics (TOG) 29 (4), 1-10, 2010 | 147 | 2010 |
| Performance evaluation of parallel large-scale lattice Boltzmann applications on three supercomputing architectures T Pohl, F Deserno, N Thurey, U Rude, P Lammers, G Wellein, T Zeiser SC'04: Proceedings of the 2004 ACM/IEEE Conference on Supercomputing, 21-21, 2004 | 143 | 2004 |
| Physics-based deep learning N Thuerey, P Holl, M Mueller, P Schnell, F Trost, K Um arXiv preprint arXiv:2109.05237, 2021 | 140 | 2021 |
Markus GrossETH Zurich, Disney Research ZurichEmail verificata su inf.ethz.ch