Improved Sampling Of Diffusion Models In Fluid Dynamics With Tweedie's Formula
Authors: Youssef Shehata, Benjamin Holzschuh, Nils Thuerey
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically demonstrate the efficacy of the proposed methods in reducing inference steps and improving the accuracy of diffusion models for fluid dynamics simulations through a diverse set of experiments, including compressible and incompressible turbulent flows in both time-dependent and steady-state settings. |
| Researcher Affiliation | Academia | Youssef Shehata Benjamin Holzschuh Nils Thuerey Technical University of Munich 85748 Garching, Germany Correspondence to: EMAIL |
| Pseudocode | Yes | Algorithm 1 Truncated Ancestral Sampling for conditional TSMs Algorithm 2 IR sampling procedure |
| Open Source Code | Yes | 1The source code is available at https://github.com/tum-pbs/tsm-ir-diffusion. |
| Open Datasets | Yes | We consider two-dimensional (2D) fluid flow test scenarios, including compressible transonic flow (Tra), incompressible forced turbulence (Fturb), and steady-state airfoil turbulence uncertainty (Air), as shown in Fig. 1. Details regarding all datasets can be found in Appendix A. Appendix A: For detailed information regarding the generation of these datasets, please refer to the corresponding papers for Tra (Kohl et al., 2024) and Air (Liu and Thuerey, 2024). |
| Dataset Splits | Yes | Forced turbulence (Fturb). ...testing is split into interpolation (int: Re = {1750}) and extrapolation (ext: Re = {100, 5000}) regions. ...Table 3: Parameter values for all datasets. ... (includes training, test ext/int/long splits with values for Ma, Re, Sequences per Param, Total Sequences, R, Total Frames) |
| Hardware Specification | Yes | Training and sampling for all test cases were carried out using NVIDIA Ge Force RTX 2080 Ti GPU. |
| Software Dependencies | No | The paper mentions 'Adam W' as an optimizer and 'Φflow' as a framework, but does not provide specific version numbers for these or other key software components like programming languages or libraries. |
| Experiment Setup | Yes | The training hyperparameters used for all test cases are presented in Table 4. ... Table 4: Summary of the training hyperparameters employed in all test cases. Parameter: Batch size, Epochs, Learning rate (start, end), Learning rate schedule, Optimizer, Weight decay, EMA decay. ... Table 5: Network architecture and diffusion-related hyperparameters used in all test cases. Parameter: βstart, βend, Schedule. |