Learning Temporally Consistent Turbulence Between Sparse Snapshots via Diffusion Models

• URL: http://arxiv.org/abs/2512.24813v1
• Date: Wed, 31 Dec 2025 11:58:48 GMT
• Title: Learning Temporally Consistent Turbulence Between Sparse Snapshots via Diffusion Models
• Authors: Mohammed Sardar, Małgorzata J. Zimoń, Samuel Draycott, Alistair Revell, Alex Skillen,
• Abstract summary: We use conditional Denoising Diffusion Probabilistic Models to reconstruct coherent dynamics between sparse snapshots of turbulent flow.<n>We analyse the generated flow sequences through the lens of statistical turbulence, examining the time-averaged turbulent energy spectra over generated sequences, kinetic and temporal decay of turbulent structures.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We investigate the statistical accuracy of temporally interpolated spatiotemporal flow sequences between sparse, decorrelated snapshots of turbulent flow fields using conditional Denoising Diffusion Probabilistic Models (DDPMs). The developed method is presented as a proof-of-concept generative surrogate for reconstructing coherent turbulent dynamics between sparse snapshots, demonstrated on a 2D Kolmogorov Flow, and a 3D Kelvin-Helmholtz Instability (KHI). We analyse the generated flow sequences through the lens of statistical turbulence, examining the time-averaged turbulent kinetic energy spectra over generated sequences, and temporal decay of turbulent structures. For the non-stationary Kelvin-Helmholtz Instability, we assess the ability of the proposed method to capture evolving flow statistics across the most strongly time-varying flow regime. We additionally examine instantaneous fields and physically motivated metrics at key stages of the KHI flow evolution.

Related papers

• Breaking the Bottlenecks: Scalable Diffusion Models for 3D Molecular Generation [0.0]
  Diffusion models have emerged as a powerful class of generative models for molecular design.<n>Their use remains constrained by long sampling trajectories, variance in the reverse process, and limited structural awareness in denoising dynamics.<n>The Directly Denoising Diffusion Model mitigates these inefficiencies by replacing reverse MCMC updates with deterministic denoising step.
  arXiv  Detail & Related papers  (2026-01-13T20:09:44Z)
• On Exact Editing of Flow-Based Diffusion Models [97.0633397035926]
  We propose Conditioned Velocity Correction (CVC) to reformulate flow-based editing as a distribution transformation problem driven by a known source prior.<n>CVC rethinks the role of velocity in inter-distribution transformation by introducing a dual-perspective velocity conversion mechanism.<n>We show that CVC consistently achieves superior fidelity, better semantic alignment, and more reliable editing behavior across diverse tasks.
  arXiv  Detail & Related papers  (2025-12-30T06:29:20Z)
• Unleashing Temporal Capacity of Spiking Neural Networks through Spatiotemporal Separation [67.69345363409835]
  Spiking Neural Networks (SNNs) are considered naturally suited for temporal processing, with membrane potential propagation widely regarded as the core temporal modeling mechanism.<n>We design Non-Stateful (NS) models progressively removing membrane propagation to its stage-wise role. Experiments reveal a counterintuitive phenomenon: moderate removal in shallow layers improves performance, while excessive removal causes collapse.
  arXiv  Detail & Related papers  (2025-12-05T07:05:53Z)
• FlowMo: Variance-Based Flow Guidance for Coherent Motion in Video Generation [51.110607281391154]
  FlowMo is a training-free guidance method for enhancing motion coherence in text-to-video models.<n>It estimates motion coherence by measuring the patch-wise variance across the temporal dimension and guides the model to reduce this variance dynamically during sampling.
  arXiv  Detail & Related papers  (2025-06-01T19:55:33Z)
• Dynamical Diffusion: Learning Temporal Dynamics with Diffusion Models [71.63194926457119]
  We introduce Dynamical Diffusion (DyDiff), a theoretically sound framework that incorporates temporally aware forward and reverse processes.<n>Experiments across scientifictemporal forecasting, video prediction, and time series forecasting demonstrate that Dynamical Diffusion consistently improves performance in temporal predictive tasks.
  arXiv  Detail & Related papers  (2025-03-02T16:10:32Z)
• Upstream flow geometries can be uniquely learnt from single-point turbulence signatures [0.21847754147782888]
  We train random forest multiclass classifier models on a vector of invariants derived from time-series data.<n>Remarkably, the algorithm was able to identify the orifice shape with 100% accuracy and 100% precision.
  arXiv  Detail & Related papers  (2024-12-14T00:55:34Z)
• Unfolding Time: Generative Modeling for Turbulent Flows in 4D [49.843505326598596]
  This work introduces 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. This advancement opens doors for the application of generative modeling in analyzing the temporal evolution of turbulent flows.
  arXiv  Detail & Related papers  (2024-06-17T10:21:01Z)
• Gaussian Interpolation Flows [11.340847429991525]
  This work investigates the well-posedness of simulation-free continuous normalizing flows built on Gaussian denoising. We establish the Lipschitz regularity of the flow velocity field, the existence and uniqueness of the flow, and the continuity of the flow map. We also study the stability of these flows in source distributions and perturbations of the velocity field, using the quadratic Wasserstein distance as a metric.
  arXiv  Detail & Related papers  (2023-11-20T00:59:20Z)
• Benchmarking Autoregressive Conditional Diffusion Models for Turbulent Flow Simulation [26.520247496906492]
  In this work, we analyze if fully data-driven fluid solvers that utilize an autoregressive rollout based on conditional diffusion models are a viable option to address this challenge.<n>To quantitatively and qualitatively benchmark the performance of various flow prediction approaches, three challenging 2D scenarios including incompressible and transonic flows, as well as isotropic turbulence are employed.<n>We find that even simple diffusion-based approaches can outperform multiple established flow prediction methods in terms of accuracy and temporal stability, while being on par with state-of-the-art stabilization techniques like unrolling at training time.
  arXiv  Detail & Related papers  (2023-09-04T18:01:42Z)
• ChiroDiff: Modelling chirographic data with Diffusion Models [132.5223191478268]
  We introduce a powerful model-class namely "Denoising Diffusion Probabilistic Models" or DDPMs for chirographic data. Our model named "ChiroDiff", being non-autoregressive, learns to capture holistic concepts and therefore remains resilient to higher temporal sampling rate.
  arXiv  Detail & Related papers  (2023-04-07T15:17:48Z)
• Predicting the temporal dynamics of turbulent channels through deep learning [0.0]
  We aim to assess the capability of neural networks to reproduce the temporal evolution of a minimal turbulent channel flow. Long-short-term-memory (LSTM) networks and a Koopman-based framework (KNF) are trained to predict the temporal dynamics of the minimal-channel-flow modes.
  arXiv  Detail & Related papers  (2022-03-02T09:31:03Z)
• A Numerical Proof of Shell Model Turbulence Closure [41.94295877935867]
  We present a closure, based on deep recurrent neural networks, that quantitatively reproduces, within statistical errors, Eulerian and Lagrangian structure functions and the intermittent statistics of the energy cascade. Our results encourage the development of similar approaches for 3D Navier-Stokes turbulence.
  arXiv  Detail & Related papers  (2022-02-18T16:31:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.