Search Results for author:
Found 8 papers, 4 papers with code
Exploring Physical Latent Spaces for High-Resolution Flow Restoration
We explore training deep neural network models in conjunction with physics simulations via partial differential equations (PDEs), using the simulated degrees of freedom as latent space for a neural network.
Autonomous Shaping of Latent-Spaces from Reduced PDEs for Physical Neural Networks
In contrast to previous work, we do not constrain the PDE solver but instead give the neural network complete freedom to shape the PDE solutions as degrees of freedom of a latent space.
Physics-based Deep Learning
This digital book contains a practical and comprehensive introduction of everything related to deep learning in the context of physical simulations.
ScalarFlow: A Large-Scale Volumetric Data Set of Real-world Scalar Transport Flows for Computer Animation and Machine Learning
In this paper, we present ScalarFlow, a first large-scale data set of reconstructions of real-world smoke plumes.
Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers
Finding accurate solutions to partial differential equations (PDEs) is a crucial task in all scientific and engineering disciplines.
Learning Similarity Metrics for Numerical Simulations
We propose a neural network-based approach that computes a stable and generalizing metric (LSiM) to compare data from a variety of numerical simulation sources.
Learning Time-Aware Assistance Functions for Numerical Fluid Solvers
While our approach is very general and applicable to arbitrary partial differential equation models, we specifically highlight gains in accuracy for fluid flow simulations.
Liquid Splash Modeling with Neural Networks
We use neural networks to model the regression of splash formation using a classifier together with a velocity modifier.
