mlsanityData to 14 Oct 2026

A Denoising Diffusion Model for Fluid Field Prediction

arXiv 2301.11661 ↗First posted 27 Jan 2023cs.LG physics.flu-dyncited by 32

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

Abstract (arXiv)

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.

Key facts. Cited by 32 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 27 of its 42 references that are in the map. First posted on arXiv 27 Jan 2023.

Builds on

27 of 42 references.

Closest ancestors · 1

Same direction, newest first

  1. 2022A Physics-informed Diffusion Model for High-fidelity Flow Field Reconstruction26 Nov

Other references · 26

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  4. 2020Denoising Diffusion Implicit Modelscited by 9,781
  5. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
  6. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
  7. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  8. 2022Generative Adversarial Networkscited by 5,627
  9. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,101
  10. 2013Auto-Encoding Variational Bayescited by 4,053
  11. 2020Fourier Neural Operator for Parametric Partial Differential Equationscited by 2,908
  12. 2021Palette: Image-to-Image Diffusion Modelscited by 1,300
14 more
  1. 2021Cascaded Diffusion Models for High Fidelity Image Generationcited by 1,163
  2. 2014NICE: Non-linear Independent Components Estimationcited by 1,058
  3. 2021Vector Quantized Diffusion Model for Text-to-Image Synthesiscited by 774
  4. 2021Machine learning accelerated computational fluid dynamicscited by 471
  5. 2021Gotta Go Fast When Generating Data with Score-Based Modelscited by 219
  6. 2018Deep Fluids: A Generative Network for Parameterized Fluid Simulationscited by 121
  7. 2021Score-based Generative Neural Networks for Large-Scale Optimal Transportcited by 60
  8. 2021Towards extraction of orthogonal and parsimonious non-linear modes from turbulent flowscited by 45
  9. 2018tempoGAN: A Temporally Coherent, Volumetric GAN for Super-resolution Fluid Flowcited by 44
  10. 2017Deep Learning the Physics of Transport Phenomenacited by 26
  11. 2021Generative Modeling of Turbulencecited by 26
  12. 2020Data-driven modelling of nonlinear spatio-temporal fluid flows using a deep convolutional generative adversarial networkcited by 16
  13. 2022DualSmoke: Sketch-Based Smoke Illustration Design with Two-Stage Generative Modelcited by 6
  14. 2019Parameter-Conditioned Sequential Generative Modeling of Fluid Flowscited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 7; 2024: 9; 2025: 9; 2026: 7.

32 papers.

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  1. 2024DiffusionPDE: Generative PDE-Solving Under Partial Observationcited by 108
  2. 2023Benchmarking Autoregressive Conditional Diffusion Models for Turbulent Flow Simulationcited by 71
  3. 2024On conditional diffusion models for PDE simulationscited by 60
  4. 2023CoCoGen: Physically-Consistent and Conditioned Score-based Generative Models for Forward and Inverse Problemscited by 56
  5. 2025Physics-Constrained Flow Matching: Sampling Generative Models with Hard Constraintscited by 52
  6. 2023From Zero to Turbulence: Generative Modeling for 3D Flow Simulationcited by 49
  7. 2024Continuous Ensemble Weather Forecasting with Diffusion modelscited by 30
  8. 2024Text2PDE: Latent Diffusion Models for Accessible Physics Simulationcited by 25
  9. 2024Data-Driven Stochastic Closure Modeling via Conditional Diffusion Model and Neural Operatorcited by 12
  10. 2025A Probabilistic Approach to Wildfire Spread Prediction Using a Denoising Diffusion Surrogate Modelcited by 11
  11. 2025Guiding diffusion models to reconstruct flow fields from sparse datacited by 11
  12. 2023Turbulence Scaling from Deep Learning Diffusion Generative Modelscited by 8
20 more
  1. 2024Generating Physical Dynamics under Priorscited by 6
  2. 2023Diffusion model based data generation for partial differential equationscited by 5
  3. 2026Particle-Guided Diffusion Models for Partial Differential Equationscited by 5
  4. 2026Physics-informed diffusion models in spectral spacecited by 5
  5. 2023Hyena Neural Operator for Partial Differential Equationscited by 4
  6. 2024Diffusion models as probabilistic neural operators for recovering unobserved states of dynamical systemscited by 4
  7. 2024Accelerating Simulation of Two-Phase Flows with Neural PDE Surrogatescited by 3
  8. 2026Adaptive Diffusion Posterior Sampling for Data and Model Fusion of Complex Nonlinear Dynamical Systemscited by 3
  9. 2023The Universal Statistical Structure and Scaling Laws of Chaos and Turbulencecited by 2
  10. 2025Autoregressive regularized score-based diffusion models for multi-scenarios fluid flow predictioncited by 2
  11. 2025PIRF: Physics-Informed Reward Fine-Tuning for Diffusion Modelscited by 2
  12. 2025Reframing Generative Models for Physical Systems using Stochastic Interpolantscited by 2
  13. 2025Generative Urban Flow Modeling: From Geometry to Airflow with Graph Diffusioncited by 2
  14. 2025Efficient probabilistic surrogate modeling techniques for partially-observed large-scale dynamical systemscited by 1
  15. 2024Efficient Probabilistic Modeling of Crystallization at Mesoscopic Scalecited by 0
  16. 2025FoilDiff: A Hybrid Transformer Backbone for Diffusion-based Modelling of 2D Airfoil Flow Fieldscited by 0
  17. 2026Coupled Diffusion-Encoder Models for Reconstruction of Flow Fieldscited by 0
  18. 2026UniFluids: Unified Neural Operator Learning with Conditional Flow-matchingcited by 0
  19. 2026Modelling Gas-Phase Reaction Kinetics with Guided Particle Diffusion Samplingcited by 0
  20. 2026Sampling from Neural Operator Priors to Solve Partial Differential Equationscited by 0

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  3. 2026Fluid-DiT: Graph-Free Diffusion Transformers for Fluid Flow Simulations Learning
  4. 2024PiRD: Physics-informed Residual Diffusion for Flow Field Reconstruction
  5. 2025Optimization Benchmark for Diffusion Models on Dynamical Systems
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