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A Denoising Diffusion Model for Fluid Field Prediction

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arxiv 2301.11661 v2 pith:ZK554XK7 submitted 2023-01-27 cs.LG physics.flu-dyn

Gefan Yang , Stefan Sommer This is my paper · ORCID
classification cs.LGphysics.flu-dyn
keywords modelfluiddatadiffusiondenoisingpredictionpredictionssystem
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We propose a novel denoising diffusion generative model for predicting nonlinear fluid fields named FluidDiff. By performing a diffusion process, the model is able to learn a complex representation of the high-dimensional dynamic system, and then Langevin sampling is used to generate predictions for the flow state under specified initial conditions. The model is trained with finite, discrete fluid simulation data. We demonstrate that our model has the capacity to model the distribution of simulated training data and that it gives accurate predictions on the test data. Without encoded prior knowledge of the underlying physical system, it shares competitive performance with other deep learning models for fluid prediction, which is promising for investigation on new computational fluid dynamics methods.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Physics-informed diffusion models in spectral space

    cs.LG 2026-02 conditional novelty 6.0 of 10

    A spectral-latent diffusion model with physics and observation guidance at inference solves forward and inverse PDE problems from sparse data with claimed large speedups.

  2. Particle-Guided Diffusion Models for Partial Differential Equations

    cs.LG 2026-01 conditional novelty 6.0 of 10

    A guided diffusion sampling method using Sequential Monte Carlo and a second-order stochastic proposal with PDE-residual guidance reduces reconstruction error on several PDE benchmarks compared with DiffusionPDE.

  3. Autoregressive regularized score-based diffusion models for multi-scenarios fluid flow prediction

    cs.LG 2025-05 conditional novelty 6.0 of 10

    A regularized autoregressive score-based diffusion model predicts turbulent flows across multiple scenarios, with the variance-preserving SDE formulation performing best.

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