196 citations · 591 across the 35 of their papers we have counts for
4 papers · 1 filter
Canonical convolutional neural networks
Lokesh Veeramacheneni, Moritz Wolter, Reinhard Klein +1
We introduce canonical weight normalization for convolutional neural networks. Inspired by the canonical tensor decomposition, we express the weight tensors in so-called canonical…
Spline-PINN: Approaching PDEs without Data using Fast, Physics-Informed Hermite-Spline CNNs
Nils Wandel, Michael Weinmann, Michael Neidlin +1
Partial Differential Equations (PDEs) are notoriously difficult to solve. In general, closed-form solutions are not available and numerical approximation schemes are computationall…
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…
Orthogonal Wasserstein GANs
Jan Müller, Reinhard Klein, Michael Weinmann
Wasserstein-GANs have been introduced to address the deficiencies of generative adversarial networks (GANs) regarding the problems of vanishing gradients and mode collapse during t…