mlsanityData to 14 Oct 2026

Unfolding Time: Generative Modeling for Turbulent Flows in 4D

arXiv 2406.11390 ↗First posted 17 Jun 2024physics.flu-dyn cs.LGcited by 4

On the map: Diffusion models → Flow matching, time series and science

Abstract (arXiv)

A recent study in turbulent flow simulation demonstrated the potential of generative diffusion models for fast 3D surrogate modeling. This approach eliminates the need for specifying initial states or performing lengthy simulations, significantly accelerating the process. While adept at sampling individual frames from the learned manifold of turbulent flow states, the previous model lacks the capability to generate sequences, hindering analysis of dynamic phenomena. This work addresses this limitation by introducing a 4D generative diffusion model and a physics-informed guidance technique that enables the generation of realistic sequences of flow states. Our findings indicate that the proposed method can successfully sample entire subsequences from the turbulent manifold, even though generalizing from individual frames to sequences remains a challenging task. This advancement opens doors for the application of generative modeling in analyzing the temporal evolution of turbulent flows, providing valuable insights into their complex dynamics.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Placed in Flow matching, time series and science, one of 14 directions in Diffusion models. Builds on 15 of its 23 references that are in the map. First posted on arXiv 17 Jun 2024.

Builds on

15 of 23 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting21 Jul
  2. 2022Equivariant Diffusion for Molecule Generation in 3D31 Mar

Other references · 13

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  4. 2020Fourier Neural Operator for Parametric Partial Differential Equationscited by 2,908
  5. 2022Video Diffusion Modelscited by 2,305
  6. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,476
  7. 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,436
  8. 2021Machine learning accelerated computational fluid dynamicscited by 471
  9. 2015Delving Deeper into Convolutional Networks for Learning Video Representationscited by 148
  10. 2023From Zero to Turbulence: Generative Modeling for 3D Flow Simulationcited by 49
  11. 2022Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networkscited by 34
  12. 2023Add and Thin: Diffusion for Temporal Point Processescited by 23
1 more
  1. 2023Assessing Robustness via Score-Based Adversarial Image Generationcited by 6

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 1; 2026: 1.

4 papers.

  1. 2024Generative prediction of flow fields around an obstacle using the diffusion modelcited by 6
  2. 2025Generative Modeling with Bayesian Sample Inferencecited by 4
  3. 2024Accelerating the discovery of steady-states of planetary interior dynamics with machine learningcited by 3
  4. 2026Coupled Diffusion-Encoder Models for Reconstruction of Flow Fieldscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023Bayesian Conditional Diffusion Models for Versatile Spatiotemporal Turbulence Generation
  2. 2025Physics-aware generative models for turbulent fluid flows through energy-consistent stochastic interpolants
  3. 2025Guiding diffusion models to reconstruct flow fields from sparse data
  4. 2024Generative Learning of the Solution of Parametric Partial Differential Equations Using Guided Diffusion Models and Virtual Observations
  5. 2023Turbulence Scaling from Deep Learning Diffusion Generative Models
  6. 2025Learning Temporally Consistent Turbulence Between Sparse Snapshots via Diffusion Models
  7. 2024Generative AI for fast and accurate statistical computation of fluids
  8. 2024Comparison of Generative Learning Methods for Turbulence Surrogates