5 papers
P3D: Scalable Neural Surrogates for High-Resolution 3D Physics Simulations with Global Context
Benjamin Holzschuh, Georg Kohl, Florian Redinger +1
We present a scalable framework for learning deterministic and probabilistic neural surrogates for high-resolution 3D physics simulations. We introduce a hybrid CNN-Transformer bac…
PDE-Transformer: Efficient and Versatile Transformers for Physics Simulations
Benjamin Holzschuh, Qiang Liu, Georg Kohl +1
We introduce PDE-Transformer, an improved transformer-based architecture for surrogate modeling of physics simulations on regular grids. We combine recent architectural improvement…
Benchmarking Autoregressive Conditional Diffusion Models for Turbulent Flow Simulation
Georg Kohl, Li-Wei Chen, Nils Thuerey
Simulating turbulent flows is crucial for a wide range of applications, and machine learning-based solvers are gaining increasing relevance. However, achieving temporal stability w…
Learning Similarity Metrics for Volumetric Simulations with Multiscale CNNs
Georg Kohl, Li-Wei Chen, Nils Thuerey
Simulations that produce three-dimensional data are ubiquitous in science, ranging from fluid flows to plasma physics. We propose a similarity model based on entropy, which allows…
Learning Similarity Metrics for Numerical Simulations
Georg Kohl, Kiwon Um, Nils Thuerey
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. We focus on scalar…