most citedComplex Valued Gated Auto-encoder for Video Frame Prediction

1 citations · 1 across the 2 of their papers we have counts for

collaborators

5 papers

physics.flu-dyn2020

Teaching the Incompressible Navier-Stokes Equations to Fast Neural Surrogate Models in 3D

Nils Wandel, Michael Weinmann, Reinhard Klein

Physically plausible fluid simulations play an important role in modern computer graphics and engineering. However, in order to achieve real-time performance, computational speed n…

cs.CV2020

Robust Skeletonization for Plant Root Structure Reconstruction from MRI

Jannis Horn, Yi Zhao, Nils Wandel +3

Structural reconstruction of plant roots from MRI is challenging, because of low resolution and low signal-to-noise ratio of the 3D measurements which may lead to disconnectivities…

cs.LG2020

Learning Incompressible Fluid Dynamics from Scratch -- Towards Fast, Differentiable Fluid Models that Generalize

Nils Wandel, Michael Weinmann, Reinhard Klein

Fast and stable fluid simulations are an essential prerequisite for applications ranging from computer-generated imagery to computer-aided design in research and development. Howev…

cs.CV2020

3D U-Net for Segmentation of Plant Root MRI Images in Super-Resolution

Yi Zhao, Nils Wandel, Magdalena Landl +2

Magnetic resonance imaging (MRI) enables plant scientists to non-invasively study root system development and root-soil interaction. Challenging recording conditions, such as low r…

cs.CV20191 cited

Complex Valued Gated Auto-encoder for Video Frame Prediction

Niloofar Azizi, Nils Wandel, Sven Behnke

In recent years, complex valued artificial neural networks have gained increasing interest as they allow neural networks to learn richer representations while potentially incorpora…