Nils Thürey

Computer Vision and Pattern Recognition · with Prantl · 2008–2020

A researcher in the Science corpus: 13 papers; trusts 711 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 206 citations into it; #7,180 of 21,575 by trust here, #7,340 by citations
• Computational Mechanics — 120 citations into it; #2,301 of 10,001 by trust here, #2,112 by citations
• Artificial Intelligence — 96 citations into it; #21,089 of 35,345 by trust here, #20,377 by citations
• Computer Graphics and Computer-Aided Design — 84 citations into it; #360 of 1,584 by trust here, #503 by citations
• Statistical and Nonlinear Physics — 63 citations into it; #5,403 of 10,127 by trust here, #2,708 by citations
• Generative Models — 45 citations into it
• Control and Systems Engineering — 32 citations into it
• AI for Physical Sciences — 27 citations into it; #145 of 247 by trust here, #215 by citations

Whom Nils Thürey trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Alex Krizhevsky
4. Kaiming He
5. Yann LeCun
6. Yoshua Bengio
7. Karen Simonyan
8. Ilya Sutskever
9. Radford M. Neal
10. Ross B. Girshick

Who trusts Nils Thürey overall

1. Steffen Wiewel
2. Lukas Prantl
3. Moritz Becher
4. Kiwon Um
5. Cheng Yang
6. Zhongqing Han
7. Boris Bonev
8. Shuhao Cao
9. Philipp Holl
10. Zherong Pan

Papers from Nils Thürey's perspective

Ordered by how strongly the researchers Nils Thürey trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Latent Space Physics: Towards Learning the Temporal Evolution of Fluid Flow (2018) — 321 citations
• Lagrangian Fluid Simulation with Continuous Convolutions (2020) — 218 citations
• Learning to Control PDEs with Differentiable Physics (2020) — 231 citations
• Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers (2020) — 363 citations
• Well, how accurate is it? A Study of Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations (2018) — 22 citations
• Deep Fluids: A Generative Network for Parameterized Fluid Simulations (2018) — 436 citations
• Data-driven synthesis of smoke flows with CNN-based feature descriptors (2017) — 281 citations
• Pre-computed Liquid Spaces with Generative Neural Networks and Optical Flow (2017) — 15 citations
• Generating Liquid Simulations with Deformation-aware Neural Networks (2017) — 16 citations
• Narrow Band FLIP for Liquid Simulations (2016) — 82 citations
• A Dimension‐reduced Pressure Solver for Liquid Simulations (2015) — 34 citations
• Blending liquids (2014) — 60 citations
• Wavelet turbulence for fluid simulation (2008) — 265 citations