mlsanityData to 14 Oct 2026

Efficient probabilistic surrogate modeling techniques for partially-observed large-scale dynamical systems

arXiv 2511.04641 ↗First posted 6 Nov 2025cs.LGcited by 1

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

Abstract (arXiv)

This paper is concerned with probabilistic techniques for forecasting dynamical systems described by partial differential equations (such as, for example, the Navier-Stokes equations). In particular, it is investigating and comparing various extensions to the flow matching paradigm that reduce the number of sampling steps. In this regard, it compares direct distillation, progressive distillation, adversarial diffusion distillation, Wasserstein GANs and rectified flows. Moreover, experiments are conducted on a set of challenging systems. In particular, we also address the challenge of directly predicting 2D slices of large-scale 3D simulations, paving the way for efficient inflow generation for solvers.

Key facts. Cited by 1 paper 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 33 of its 43 references that are in the map. First posted on arXiv 6 Nov 2025.

Builds on

33 of 43 references.

Closest ancestors · 12

Same direction, newest first

  1. 2025Diff-SPORT: Diffusion-based Sensor Placement Optimization and Reconstruction of Turbulent flows in urban environments30 May
  2. 2025Generative Latent Neural PDE Solver using Flow Matching28 Mar
  3. 2025Probabilistic Forecasting via Autoregressive Flow Matching13 Mar
  4. 2024Generator Matching: Generative modeling with arbitrary Markov processes27 Oct
  5. 2024On conditional diffusion models for PDE simulations21 Oct
  6. 2024DiffFluid: Plain Diffusion Models are Effective Predictors of Flow Dynamics20 Sep
  7. 2023InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generation12 Sep
  8. 2023Benchmarking Autoregressive Conditional Diffusion Models for Turbulent Flow Simulation4 Sep
  9. 2023DYffusion: A Dynamics-informed Diffusion Model for Spatiotemporal Forecasting3 Jun
  10. 2023A Denoising Diffusion Model for Fluid Field Prediction27 Jan
  11. 2022Flow Matching for Generative Modeling6 Oct
  12. 2022Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow7 Sep

Other references · 21

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2020Denoising Diffusion Implicit Modelscited by 9,781
  3. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
  4. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  5. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,101
  6. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,761
  7. 2020Fourier Neural Operator for Parametric Partial Differential Equationscited by 2,908
  8. 2023Consistency Modelscited by 1,822
  9. 2023Adversarial Diffusion Distillationcited by 759
  10. 2020Neural Operator: Graph Kernel Network for Partial Differential Equationscited by 734
  11. 2021Tackling the Generative Learning Trilemma with Denoising Diffusion GANscited by 516
  12. 2023Improved Techniques for Training Consistency Modelscited by 439
9 more
  1. 2022PDEBENCH: An Extensive Benchmark for Scientific Machine Learningcited by 413
  2. 2024SDXL-Lightning: Progressive Adversarial Diffusion Distillationcited by 266
  3. 2018Data-Driven Forecasting of High-Dimensional Chaotic Systems with Long Short-Term Memory Networkscited by 155
  4. 2024The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learningcited by 145
  5. 2023ACE: A fast, skillful learned global atmospheric model for climate predictioncited by 56
  6. 2023OceanNet: A principled neural operator-based digital twin for regional oceanscited by 24
  7. 2025Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstructioncited by 8
  8. 2024Generative Learning for Forecasting the Dynamics of Complex Systemscited by 6
  9. 2025Surrogate Modeling of 3D Rayleigh-Benard Convection with Equivariant Autoencoderscited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026StocBench: A Benchmark for Generative Modeling of Stochastic Dynamicscited by 2

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  3. 2024Generative Learning of the Solution of Parametric Partial Differential Equations Using Guided Diffusion Models and Virtual Observations
  4. 2025Residual-augmented flow matching operators for probabilistic partial differential equations
  5. 2025Rectified Flows for Fast Multiscale Fluid Flow Modeling
  6. 2024Comparison of Generative Learning Methods for Turbulence Surrogates
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  8. 2025Learning Distributions of Complex Fluid Simulations with Diffusion Graph Networks