| 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 | 127 | 2020 |
| Learning to control pdes with differentiable physics P Holl, V Koltun, N Thuerey arXiv preprint arXiv:2001.07457, 2020 | 124 | 2020 |
| Holography of wi-fi radiation PM Holl, F Reinhard Physical review letters 118 (18), 183901, 2017 | 67 | 2017 |
| Physics-based deep learning N Thuerey, P Holl, M Mueller, P Schnell, F Trost, K Um arXiv preprint arXiv:2109.05237, 2021 | 51 | 2021 |
| 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, 1-9, 2019 | 36 | 2019 |
| phiflow: A differentiable pde solving framework for deep learning via physical simulations P Holl, V Koltun, K Um, N Thuerey NeurIPS workshop 2, 2020 | 19 | 2020 |
| Half-inverse gradients for physical deep learning P Schnell, P Holl, N Thuerey arXiv preprint arXiv:2203.10131, 2022 | 5 | 2022 |
| Learning to control pdes with differentiable physics, 2020 P Holl, V Koltun, N Thuerey URL https://arxiv. org/abs, 2001 | 4 | 2001 |
| Simulating Liquids with Graph Networks J Klimesch, P Holl, N Thuerey arXiv preprint arXiv:2203.07895, 2022 | 1 | 2022 |
| Scale-invariant Learning by Physics Inversion P Holl, V Koltun, N Thuerey Advances in Neural Information Processing Systems 35, 5390-5403, 2022 | | 2022 |
| Physical Gradients for Deep Learning P Holl, N Thuerey, V Koltun | | 2021 |
| Differentiable Physics for Improving the Accuracy of Iterative PDE-Solvers with Neural Networks K Um, YR Fei, P Holl, R Brand, N Thuerey | | |
| Learning Time-Aware Assistance Functions for Numerical Fluid Solvers K Um, YR Fei, P Holl, N Thuerey | | |
