| Learning to control pdes with differentiable physics P Holl, V Koltun, N Thuerey arXiv preprint arXiv:2001.07457, 2020 | 98 | 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 | 79 | 2020 |
| Holography of wi-fi radiation PM Holl, F Reinhard Physical review letters 118 (18), 183901, 2017 | 59 | 2017 |
| Deep learning based pulse shape discrimination for germanium detectors P Holl, L Hauertmann, B Majorovits, O Schulz, M Schuster, AJ Zsigmond The European Physical Journal C 79 (6), 1-9, 2019 | 27 | 2019 |
| Physics-based deep learning N Thuerey, P Holl, M Mueller, P Schnell, F Trost, K Um arXiv preprint arXiv:2109.05237, 2021 | 21 | 2021 |
| phiflow: A differentiable pde solving framework for deep learning via physical simulations P Holl, V Koltun, K Um, N Thuerey NeurIPS Workshop 2, 2020 | 13 | 2020 |
| Simulating Liquids with Graph Networks J Klimesch, P Holl, N Thuerey arXiv preprint arXiv:2203.07895, 2022 | 1 | 2022 |
| Physical Gradients for Deep Learning P Holl, V Koltun, N Thuerey arXiv preprint arXiv:2109.15048, 2021 | 1 | 2021 |
| Half-Inverse Gradients for Physical Deep Learning P Schnell, P Holl, N Thuerey arXiv preprint arXiv:2203.10131, 2022 | | 2022 |
| Learning Time-Aware Assistance Functions for Numerical Fluid Solvers K Um, YR Fei, P Holl, N Thuerey | | 2020 |
| Differentiable Physics for Improving the Accuracy of Iterative PDE-Solvers with Neural Networks K Um, YR Fei, P Holl, R Brand, N Thuerey | | |
