From Zero to Turbulence: Generative Modeling for 3D Flow Simulation
Authors: Marten Lienen, David Lüdke, Jan Hansen-Palmus, Stephan Günnemann
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | For our experiments, we introduce a challenging 3D turbulence dataset of high-resolution flows and detailed vortex structures caused by various objects and derive two novel sample evaluation metrics for turbulent flows. On this dataset, we show that our generative model captures the distribution of turbulent flows caused by unseen objects and generates high-quality, realistic samples amenable for downstream applications without access to any initial state. |
| Researcher Affiliation | Academia | Marten Lienen, David Lüdke, Jan Hansen-Palmus, Stephan Günnemann Department of Informatics & Munich Data Science Institute Technical University of Munich, Germany {m.lienen,d.luedke,j.hansen-palmus,s.guennemann}@tum.de |
| Pseudocode | No | The paper describes the model (DDPM, U-Net, transformer) and training details in prose, but it does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Find code and data at https://cs.cit.tum.de/daml/generative-turbulence. |
| Open Datasets | Yes | To evaluate the viability of generative modeling for 3D turbulent flows, we have generated a challenging new dataset. The dataset consists of 45 simulations of an incompressible flow through a 0.4 m 0.1 m 0.1 m channel at 20 m s 1, which results in a Reynold s number of Re = 2 105, well into the turbulent regime. Find code and data at https://cs.cit.tum.de/daml/generative-turbulence. |
| Dataset Splits | Yes | We split the simulations into 27 for training, 9 for validation, and 9 for testing to verify that models can generalize to unseen objects in the flow. |
| Hardware Specification | Yes | All model times represent the minimum achieved time on an NVIDIA A100, and we measured the solver time with 16-core parallelism on an Intel Xeon E5-2630. |
| Software Dependencies | No | The paper mentions using Open FOAM as a CFD solver but does not specify its version. While PyTorch is referenced, no specific version of PyTorch or other software dependencies used for the experiments is provided in the main text or appendices. |
| Experiment Setup | Yes | We chose N = 500 and the log-linear signal-to-noise ratio (SNR) schedule from (Kingma et al., 2021) that scales βn such that the log-SNR of the data falls linearly from 1 103 to 1 10 5. We trained Turb Diff from 3 different seeds for 10 epochs with a batch size of 6. The optimizer is RAdam with a learning rate of 1 10 4 and and exponential learning rate decay to 1 10 6 over those epochs. |