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Philipp Holl
Philipp Holl
Verified email at tum.de
Title
Cited by
Cited by
Year
Learning to control pdes with differentiable physics
P Holl, V Koltun, N Thuerey
arXiv preprint arXiv:2001.07457, 2020
982020
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
792020
Holography of wi-fi radiation
PM Holl, F Reinhard
Physical review letters 118 (18), 183901, 2017
592017
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
272019
Physics-based deep learning
N Thuerey, P Holl, M Mueller, P Schnell, F Trost, K Um
arXiv preprint arXiv:2109.05237, 2021
212021
phiflow: A differentiable pde solving framework for deep learning via physical simulations
P Holl, V Koltun, K Um, N Thuerey
NeurIPS Workshop 2, 2020
132020
Simulating Liquids with Graph Networks
J Klimesch, P Holl, N Thuerey
arXiv preprint arXiv:2203.07895, 2022
12022
Physical Gradients for Deep Learning
P Holl, V Koltun, N Thuerey
arXiv preprint arXiv:2109.15048, 2021
12021
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
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Articles 1–11