research
∙
09/04/2023
Turbulent Flow Simulation using Autoregressive Conditional Diffusion Models
Simulating turbulent flows is crucial for a wide range of applications, ...
share
research
∙
02/28/2023
Learning to Estimate Single-View Volumetric Flow Motions without 3D Supervision
We address the challenging problem of jointly inferring the 3D flow and ...
research
∙
01/24/2023
Score Matching via Differentiable Physics
Diffusion models based on stochastic differential equations (SDEs) gradu...
research
∙
11/21/2022
Exploring Physical Latent Spaces for Deep Learning
We explore training deep neural network models in conjunction with physi...
research
∙
10/12/2022
Guaranteed Conservation of Momentum for Learning Particle-based Fluid Dynamics
We present a novel method for guaranteeing linear momentum in learned ph...
research
∙
09/06/2022
Wavelet-based Loss for High-frequency Interface Dynamics
Generating highly detailed, complex data is a long-standing and frequent...
research
∙
06/01/2022
Control of Two-way Coupled Fluid Systems with Differentiable Solvers
We investigate the use of deep neural networks to control complex nonlin...
research
∙
05/02/2022
Leveraging Stochastic Predictions of Bayesian Neural Networks for Fluid Simulations
We investigate uncertainty estimation and multimodality via the non-dete...
research
∙
05/02/2022
WeatherBench Probability: A benchmark dataset for probabilistic medium-range weather forecasting along with deep learning baseline models
WeatherBench is a benchmark dataset for medium-range weather forecasting...
research
∙
04/07/2022
TemporalUV: Capturing Loose Clothing with Temporally Coherent UV Coordinates
We propose a novel approach to generate temporally coherent UV coordinat...
research
∙
03/18/2022
Half-Inverse Gradients for Physical Deep Learning
Recent works in deep learning have shown that integrating differentiable...
research
∙
03/14/2022
Simulating Liquids with Graph Networks
Simulating complex dynamics like fluids with traditional simulators is c...
research
∙
02/14/2022
Learned Turbulence Modelling with Differentiable Fluid Solvers
In this paper, we train turbulence models based on convolutional neural ...
research
∙
02/08/2022
Learning Similarity Metrics for Volumetric Simulations with Multiscale CNNs
Simulations that produce three-dimensional data are ubiquitous in scienc...
research
∙
09/30/2021
Physical Gradients for Deep Learning
Solving inverse problems, such as parameter estimation and optimal contr...
research
∙
09/11/2021
Physics-based Deep Learning
This digital book contains a practical and comprehensive introduction of...
research
∙
09/05/2021
Towards high-accuracy deep learning inference of compressible turbulent flows over aerofoils
The present study investigates the accurate inference of Reynolds-averag...
research
∙
05/13/2021
Self-Supervised Collision Handling via Generative 3D Garment Models for Virtual Try-On
We propose a new generative model for 3D garment deformations that enabl...
research
∙
04/13/2021
Global Transport for Fluid Reconstruction with Learned Self-Supervision
We propose a novel method to reconstruct volumetric flows from sparse vi...
research
∙
11/20/2020
Neural Scene Graphs for Dynamic Scenes
Recent implicit neural rendering methods have demonstrated that it is po...
research
∙
11/20/2020
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 re...
research
∙
11/20/2020
Perceptual Evaluation of Liquid Simulation Methods
This paper proposes a novel framework to evaluate fluid simulation metho...
research
∙
06/30/2020
Data-driven Regularization via Racecar Training for Generalizing Neural Networks
We propose a novel training approach for improving the generalization in...
research
∙
06/30/2020
Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers
Finding accurate solutions to partial differential equations (PDEs) is a...
research
∙
05/16/2020
A Combined Data-driven and Physics-driven Method for Steady Heat Conduction Prediction using Deep Convolutional Neural Networks
With several advantages and as an alternative to predict physics field, ...
research
∙
03/12/2020
Latent Space Subdivision: Stable and Controllable Time Predictions for Fluid Flow
We propose an end-to-end trained neural networkarchitecture to robustly ...
research
∙
02/18/2020
Learning Similarity Metrics for Numerical Simulations
We propose a neural network-based approach that computes a stable and ge...
research
∙
02/02/2020
WeatherBench: A benchmark dataset for data-driven weather forecasting
Data-driven approaches, most prominently deep learning, have become powe...
research
∙
01/21/2020
Learning to Control PDEs with Differentiable Physics
Predicting outcomes and planning interactions with the physical world ar...
research
∙
10/04/2019
Sparse Surface Constraints for Combining Physics-based Elasticity Simulation and Correspondence-Free Object Reconstruction
We address the problem to infer physical material parameters and boundar...
research
∙
06/15/2019
Volumetric Isosurface Rendering with Deep Learning-Based Super-Resolution
Rendering an accurate image of an isosurface in a volumetric field typic...
research
∙
06/04/2019
A Multi-Pass GAN for Fluid Flow Super-Resolution
We propose a novel method to up-sample volumetric functions with generat...
research
∙
11/23/2018
Temporally Coherent GANs for Video Super-Resolution (TecoGAN)
Adversarial training has been highly successful in the context of image ...
research
∙
10/18/2018
Well, how accurate is it? A Study of Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations
With this study we investigate the accuracy of deep learning models for ...
research
∙
06/18/2018
Coupled Fluid Density and Motion from Single Views
We present a novel method to reconstruct a fluid's 3D density and motion...
research
∙
06/06/2018
Deep Fluids: A Generative Network for Parameterized Fluid Simulations
This paper presents a novel generative model to synthesize fluid simulat...
research
∙
02/27/2018
Latent-space Physics: Towards Learning the Temporal Evolution of Fluid Flow
Our work explores methods for the data-driven inference of temporal evol...
research
∙
01/29/2018
tempoGAN: A Temporally Coherent, Volumetric GAN for Super-resolution Fluid Flow
We propose a temporally coherent generative model addressing the super-r...
research
∙
05/03/2017
Data-Driven Synthesis of Smoke Flows with CNN-based Feature Descriptors
We present a novel data-driven algorithm to synthesize high-resolution f...
research
∙
04/25/2017
Pre-computed Liquid Spaces with Generative Neural Networks and Optical Flow
Liquids exhibit highly complex, non-linear behavior under changing simul...
research
∙
04/14/2017
Liquid Splash Modeling with Neural Networks
This paper proposes a new data-driven approach for modeling detailed spl...
research
∙
11/11/2016
Primal-Dual Optimization for Fluids
We apply a novel optimization scheme from the image processing and machi...
research
∙
08/30/2016
Interpolations of Smoke and Liquid Simulations
We present a novel method to interpolate smoke and liquid simulations in...
research
∙
03/29/2016
