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Philipp Holl

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2020 – today

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2022
[i6]
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  - journals/corr/abs-2203-07895
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  - https://dblp.org/rec/journals/corr/abs-2203-07895
Jonathan Klimesch, Philipp Holl, Nils Thuerey:
Simulating Liquids with Graph Networks. CoRR abs/2203.07895 (2022)
[i5]
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  - journals/corr/abs-2203-10131
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Patrick Schnell, Philipp Holl, Nils Thuerey:
Half-Inverse Gradients for Physical Deep Learning. CoRR abs/2203.10131 (2022)
2021
[i4]
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  - journals/corr/abs-2109-05237
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Nils Thuerey, Philipp Holl, Maximilian Müller, Patrick Schnell, Felix Trost, Kiwon Um:
Physics-based Deep Learning. CoRR abs/2109.05237 (2021)
[i3]
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  - journals/corr/abs-2109-15048
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Philipp Holl, Vladlen Koltun, Nils Thuerey:
Physical Gradients for Deep Learning. CoRR abs/2109.15048 (2021)
2020
[c2]
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  dblp key:
  - conf/iclr/HollTK20
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Philipp Holl, Nils Thuerey, Vladlen Koltun:
Learning to Control PDEs with Differentiable Physics. ICLR 2020
[c1]
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  - conf/nips/UmBFHT20
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Kiwon Um, Robert Brand, Yun (Raymond) Fei, Philipp Holl, Nils Thuerey:
Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers. NeurIPS 2020
[i2]
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  - journals/corr/abs-2001-07457
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Philipp Holl, Vladlen Koltun, Nils Thuerey:
Learning to Control PDEs with Differentiable Physics. CoRR abs/2001.07457 (2020)
[i1]
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  - journals/corr/abs-2007-00016
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Kiwon Um, Yun (Raymond) Fei, Philipp Holl, Robert Brand, Nils Thuerey:
Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers. CoRR abs/2007.00016 (2020)

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