Search Results for author:
Found 8 papers, 2 papers with code
Guaranteed Conservation of Momentum for Learning Particle-based Fluid Dynamics
We present a novel method for guaranteeing linear momentum in learned physics simulations.
Wavelet-based Loss for High-frequency Interface Dynamics
As an alternative, we present a new method based on a wavelet loss formulation, which remains transparent in terms of what is optimized.
Learning the shape of female breasts: an open-access 3D statistical shape model of the female breast built from 110 breast scans
We present the Regensburg Breast Shape Model (RBSM) -- a 3D statistical shape model of the female breast built from 110 breast scans acquired in a standing position, and the first publicly available.
Frequency-aware Interface Dynamics with Generative Adversarial Networks
We present a new method for reconstructing and refining complex surfaces based on physical simulations.
Lagrangian Fluid Simulation with Continuous Convolutions
We present an approach to Lagrangian fluid simulation with a new type of convolutional network.
Tranquil Clouds: Neural Networks for Learning Temporally Coherent Features in Point Clouds
Point clouds, as a form of Lagrangian representation, allow for powerful and flexible applications in a large number of computational disciplines.
Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations of Airfoil Flows
With this study we investigate the accuracy of deep learning models for the inference of Reynolds-Averaged Navier-Stokes solutions.
Generating Liquid Simulations with Deformation-aware Neural Networks
Our algorithm captures these complex phenomena in two stages: a first neural network computes a weighting function for a set of pre-computed deformations, while a second network directly generates a deformation field for refining the surface.
