Patched Flow Matching: Generative Wall-Pressure Reconstruction Beyond Training-Domain Scales from Sparse Sensors

• URL: http://arxiv.org/abs/2606.22084v1
• Date: Sat, 20 Jun 2026 15:01:37 GMT
• Title: Patched Flow Matching: Generative Wall-Pressure Reconstruction Beyond Training-Domain Scales from Sparse Sensors
• Abstract summary: We propose a generative framework that fuses short-domain numerical simulation and sparse sensor measurements.<n>Patched Flow Matching reconstructs full-resolution wall-pressure fields on a domain four times larger than the training configuration.
• Score: 4.202564595152414
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Characterizing the complete wall-pressure spectrum in turbulent wall-bounded flows requires simultaneous access to the viscous-scale high-wavenumber content and the outer-layer low-wavenumber content -- a requirement that neither short-domain direct numerical simulation (DNS) nor sparse experimental measurements alone can satisfy. We propose Patched Flow Matching (Patched FM), a generative framework that fuses these two complementary sources by learning a patch-local prior over inner-scaled wall-pressure statistics from short-domain DNS and assimilating sparse sensor measurements at inference time through training-free posterior sampling. The patch-additive decomposition of the flow matching vector field decouples the generative prior from the global domain size, enabling reconstruction on domains arbitrarily larger than the training configuration. By expressing the patch prior in inner-scaled coordinates, where high-wavenumber wall-pressure statistics are approximately Reynolds-number invariant, the framework extends to higher Reynolds numbers through hierarchical transfer learning with as few as $500$ short-domain snapshots ($2.5\%$ of the base training data) at a fraction of the scratch-training cost. Applied to compressible channel-flow DNS at $Re_τ= 180$, $500$, and $1000$, Patched FM reconstructs full-resolution wall-pressure fields on a domain four times larger than the training configuration ($L_x^L = 16πδ$ versus $L_x^S = 4πδ$) from sensor coverage as low as $0.25\%$, recovering the low-wavenumber spectral content inaccessible to short-domain DNS with high fidelity in both streamwise and spanwise directions. Zero-shot generalization to unseen Reynolds numbers and ablation studies further confirm the role of inner scaling as a physical prerequisite for data-efficient Reynolds-number transfer.

Related papers

• A priori Assessment of Tensor-Network Encoding for Isotropic Turbulent Flows [0.8634867517077641]
  matrix product state (MPS) ansatz is utilized here as a reduced-order modeling framework to construct truncated representations of isotropic turbulent flow data.<n> Velocity reconstructions achieve $99.8%$ fidelity using only $5%$ of the original DNS memory, while the scalar field reaches the same fidelity at $15%$ memory usage.
  arXiv  Detail & Related papers  (2026-08-28T21:10:10Z)
• Machine Learning-Based Reconstruction for Resistive Silicon Sensors [53.365453214937496]
  We study machine-learning-based reconstruction and compression for resistive silicon sensors.<n>We use full-waveform information from correlated pads to regularise the reconstruction and extract spatial information.<n>We first introduce recurrent neural network models based on LSTM layers, which provide a proof-of-concept implementation for full-waveform reconstruction.
  arXiv  Detail & Related papers  (2026-07-13T14:03:26Z)
• Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders [123.58723804218151]
  Vision foundation models are bottlenecked by the quadratic cost of self-attention.<n>We present C-GSPN based on 2D spatial propagation propagation.
  arXiv  Detail & Related papers  (2026-05-30T14:29:43Z)
• Morphing Through Time: Diffusion-Based Bridging of Temporal Gaps for Robust Alignment in Change Detection [51.56484100374058]
  We introduce a modular pipeline that improves spatial and temporal robustness without altering existing change detection networks.<n>A diffusion module synthesizes intermediate morphing frames that bridge large appearance gaps, enabling RoMa to estimate stepwise correspondences.<n>Experiments on LEVIR-CD, WHU-CD, and DSIFN-CD show consistent gains in both registration accuracy and downstream change detection.
  arXiv  Detail & Related papers  (2025-11-11T08:40:28Z)
• Flexiffusion: Training-Free Segment-Wise Neural Architecture Search for Efficient Diffusion Models [50.260693393896716]
  Diffusion models (DMs) are powerful generative models capable of producing high-fidelity images but constrained by high computational costs.<n>We propose Flexiffusion, a training-free NAS framework that jointly optimize generation schedules and model architectures without modifying pre-trained parameters.<n>Our work pioneers a resource-efficient paradigm for searching high-speed DMs without sacrificing quality.
  arXiv  Detail & Related papers  (2025-06-03T06:02:50Z)
• FLEX: A Backbone for Diffusion-Based Modeling of Spatio-temporal Physical Systems [51.15230303652732]
  FLEX (F Low EXpert) is a backbone architecture for generative modeling of-temporal physical systems.<n>It reduces the variance of the velocity field in the diffusion model, which helps stabilize training.<n>It achieves accurate predictions for super-resolution and forecasting tasks using as few features as two reverse diffusion steps.
  arXiv  Detail & Related papers  (2025-05-23T00:07:59Z)
• Predicting the wall-shear stress and wall pressure through convolutional neural networks [1.95992742032823]
  This study aims to assess the capability of convolution-based neural networks to predict wall quantities in a turbulent open channel flow. The predictions from the FCN are compared against the predictions from a proposed R-Net architecture. The R-Net is also able to predict the wall-shear-stress and wall-pressure fields using the velocity-fluctuation fields at $y+ = 50$.
  arXiv  Detail & Related papers  (2023-03-01T18:03:42Z)
• Transform Once: Efficient Operator Learning in Frequency Domain [69.74509540521397]
  We study deep neural networks designed to harness the structure in frequency domain for efficient learning of long-range correlations in space or time. This work introduces a blueprint for frequency domain learning through a single transform: transform once (T1)
  arXiv  Detail & Related papers  (2022-11-26T01:56:05Z)
• An AI-based Domain-Decomposition Non-Intrusive Reduced-Order Model for Extended Domains applied to Multiphase Flow in Pipes [0.0]
  We present a new AI-based non-intrusive reduced-order model within a domain decomposition framework. It is capable of making predictions for domains significantly larger than the domain used in training. The framework is applied to multiphase slug flow in a horizontal pipe for which an AI-DDNIROM is trained on high-fidelity CFD simulations.
  arXiv  Detail & Related papers  (2022-02-13T00:32:17Z)
• Predicting the near-wall region of turbulence through convolutional neural networks [0.0]
  A neural-network-based approach to predict the near-wall behaviour in a turbulent open channel flow is investigated. The fully-convolutional network (FCN) is trained to predict the two-dimensional velocity-fluctuation fields at $y+_rm target$. FCN can take advantage of the self-similarity in the logarithmic region of the flow and predict the velocity-fluctuation fields at $y+ = 50$.
  arXiv  Detail & Related papers  (2021-07-15T13:58:26Z)
• Convolutional-network models to predict wall-bounded turbulence from wall quantities [0.0]
  Two models are trained to predict the two-dimensional velocity-fluctuation fields at different wall-normal locations in a turbulent open channel flow. The first model is a fully-convolutional neural network (FCN) which directly predicts the fluctuations. The second one reconstructs the flow fields using a linear combination of orthonormal basis functions.
  arXiv  Detail & Related papers  (2020-06-22T17:57:40Z)
• Prediction of wall-bounded turbulence from wall quantities using convolutional neural networks [0.0]
  A fully-convolutional neural-network model is used to predict the streamwise velocity fields at several wall-normal locations. Various networks are trained for predictions at three inner-scaled locations.
  arXiv  Detail & Related papers  (2019-12-30T15:34:41Z)

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.