Generative modeling of granular flow on inclined planes using conditional flow matching

• URL: http://arxiv.org/abs/2604.04453v1
• Date: Mon, 06 Apr 2026 05:59:54 GMT
• Title: Generative modeling of granular flow on inclined planes using conditional flow matching
• Abstract summary: This study presents the first conditional flow matching framework for granular-flow reconstruction from sparse boundary observations.<n>A physics decoder maps the reconstructed velocity fields to stress states and energy fluctuation quantities.<n>Results demonstrate that conditional generative modeling offers a practical route for non-invasive inference of hidden bulk mechanics in granular media.
• Score: 4.880426970438706
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Granular flows govern many natural and industrial processes, yet their interior kinematics and mechanics remain largely unobservable, as experiments access only boundaries or free surfaces. Conventional numerical simulations are computationally expensive for fast inverse reconstruction, and deterministic models tend to collapse to over-smoothed mean predictions in ill-posed settings. This study, to the best of the authors' knowledge, presents the first conditional flow matching (CFM) framework for granular-flow reconstruction from sparse boundary observations. Trained on high-fidelity particle-resolved discrete element simulations, the generative model is guided at inference by a differentiable forward operator with a sparsity-aware gradient guidance mechanism, which enforces measurement consistency without hyperparameter tuning and prevents unphysical velocity predictions in non-material regions. A physics decoder maps the reconstructed velocity fields to stress states and energy fluctuation quantities, including mean stress, deviatoric stress, and granular temperature. The framework accurately recovers interior flow fields from full observation to only 16% of the informative window, and it remains effective under strongly diluted spatial resolution with only 11% of data. It also outperforms a deterministic CNN baseline in the most ill-posed reconstruction regime and provides spatially resolved uncertainty estimates through ensemble generation. These results demonstrate that conditional generative modeling offers a practical route for non-invasive inference of hidden bulk mechanics in granular media, with broader applicability for inverse problems in particulate and multiphase systems.

Related papers

• Improving Reduced-Order Rotating Detonation Engine Models with Data Assimilation and Machine Learning [2.225353152447347]
  Low-order models such as the one-dimensional Koch-Kutz model capture wave motion yet lack the expressivity for high-frequency content.<n>We use continuous data assimilation (nudging) to synchronize the Koch-Kutz solver with processed high-fidelity temperature data.<n>As the strength increases, the reduced model is progressively drawn onto the high-fidelity trajectory.
  arXiv  Detail & Related papers  (2026-09-14T19:05:23Z)
• A Shortcut to Statistically Steady-State Turbulence with Flow Matching [18.320829792574738]
  We introduce GyroFlow, a latent generative model that directly estimates steady-state statistics of gyrokinetic turbulence in 5D phase space.<n>To evaluate generation quality we propose FGyD, a distributional metric computed in the latent space of a pretrained gyrokinetic model.
  arXiv  Detail & Related papers  (2026-07-14T17:58:13Z)
• High-fidelity Modeling of Full-scale Pressurized Water Reactor Flow Fields for Machine Learning Applications [3.8347851078346427]
  This work presents a high-fidelity computational fluid dynamics (CFD) and data-driven modeling framework for assembly-level flow characterization in a pressurized water reactor.<n>A full lower-plenum and core-inlet domain was constructed using publicly available geometry and operating conditions, enabling transient simulations with pump-induced swirl boundary conditions.<n>Results show that cold-leg swirl and lower-plenum transport generate strongly heterogeneous assembly-wise inlet flow distributions, particularly near the lower core region.<n>A 3D convolutional-based inpainting model successfully recon-structed missing assembly-level mass flow rates from partial observations
  arXiv  Detail & Related papers  (2026-05-23T22:50:23Z)
• Physics-Informed Generative Solver: Bridging Data-Driven Priors and Conservation Laws for Stable Spatiotemporal Field Reconstruction [19.98127585683643]
  We introduce a physics-informed generative solver that separates stable prior learning from inference-time enforcement of conservation laws.<n>In acoustics, the method co-generates pressure and particle velocity from sparse sensors, enabling dense virtual arrays that suppress spatial aliasing.<n>The same framework generalizes to real-world ERA5 meteorological fields under extreme sparsity.
  arXiv  Detail & Related papers  (2026-05-21T11:24:48Z)
• Physics-informed reservoir characterization from bulk and extreme pressure events with a differentiable simulator [42.95123707634075]
  We introduce a physics-informed machine learning method that embeds a differentiable subsurface flow simulator directly into neural network training.<n>The network infers heterogeneous permeability fields from limited pressure observations, while training minimizes both permeability and pressure losses through the simulator, enforcing physical consistency.<n>Overall, the proposed method enables rapid, physics-consistent subsurface inversion for real-time reservoir characterization and risk-aware decision-making.
  arXiv  Detail & Related papers  (2026-04-14T20:52:45Z)
• Nowcast3D: Reliable precipitation nowcasting via gray-box learning [43.74533335638689]
  Extreme precipitation nowcasting demands high-dimensional fidelity and extended lead times, yet existing approaches remain limited.<n>We introduce a gray-box, fully three-temporal nowcasting framework that directly processes radar motions and couples physically constrained neural operators with datadriven learning.<n>The framework achieves more accurate forecasts up to three-hour lead time in a blind evaluation by 160 meteorologists.
  arXiv  Detail & Related papers  (2025-11-06T18:44:35Z)
• Physically consistent and uncertainty-aware learning of spatiotemporal dynamics [44.27000400517127]
  We introduce a physics-consistent neural operator (PCNO) that enforces physical constraints.<n>A physics-consistent projection layer within PCNO efficiently computes mass momentum conservation in Fourier space.<n>We also propose DiffPCNO, which leverages a diffusion consistency model to quantify and quantify uncertainties.
  arXiv  Detail & Related papers  (2025-10-23T22:17:21Z)
• Differential-Integral Neural Operator for Long-Term Turbulence Forecasting [43.04613533979613]
  textbfunderlineDifferential-textbfunderlineIntegral textbfunderlineNeural textbfunderlineOperator (method)<n>method explicitly models the turbulent evolution through parallel branches that learn distinct physical operators.<n>It successfully suppresses error accumulation over hundreds of timesteps, maintains high fidelity in both the vorticity fields and energy spectra, and establishes a new benchmark for physically consistent, long-range turbulence forecast.
  arXiv  Detail & Related papers  (2025-09-25T14:08:26Z)
• Physics-informed neural networks for hidden boundary detection and flow field reconstruction [0.0]
  This study presents a physics-informed neural network (PINN) framework designed to infer the presence, shape, and motion of static or moving solid boundaries.<n>The framework is validated across diverse scenarios, including incompressible Navier-Stokes and compressible Euler flows.<n>The proposed method exhibits robustness and versatility, highlighting its potential for applications when only limited experimental or numerical data are available.
  arXiv  Detail & Related papers  (2025-03-31T13:30:46Z)
• TAUDiff: Highly efficient kilometer-scale downscaling using generative diffusion models [0.0]
  It is crucial to achieve rapid turnaround, dynamical consistency, and accurate-temporal recovery for extreme weather events.<n>We propose an efficient diffusion model TAUDiff, that combines a deterministic-temporal model for mean field downscaling with a smaller generative diffusion model for recovering the fine-scale features.<n>Our approach can ensure quicker simulation of extreme events necessary for estimating associated risks and economic losses.
  arXiv  Detail & Related papers  (2024-12-18T09:05:19Z)
• Machine learning-enabled velocity model building with uncertainty quantification [0.41942958779358674]
  Accurately characterizing migration velocity models is crucial for a wide range of geophysical applications. Traditional velocity model building methods are powerful but often struggle with the inherent complexities of the inverse problem. We propose a scalable methodology that integrates generative modeling, in the form of Diffusion networks, with physics-informed summary statistics.
  arXiv  Detail & Related papers  (2024-11-11T01:36:48Z)
• A conditional latent autoregressive recurrent model for generation and forecasting of beam dynamics in particle accelerators [46.348283638884425]
  We propose a two-step unsupervised deep learning framework named as Latent Autoregressive Recurrent Model (CLARM) for learning dynamics of charged particles in accelerators. The CLARM can generate projections at various accelerator sampling modules by capturing and decoding the latent space representation. The results demonstrate that the generative and forecasting ability of the proposed approach is promising when tested against a variety of evaluation metrics.
  arXiv  Detail & Related papers  (2024-03-19T22:05:17Z)
• Multi-Modal Learning-based Reconstruction of High-Resolution Spatial Wind Speed Fields [46.72819846541652]
  We propose a framework based on Vari Data Assimilation and Deep Learning concepts. This framework is applied to recover rich-in-time, high-resolution information on sea surface wind speed.
  arXiv  Detail & Related papers  (2023-12-14T13:40:39Z)
• Ensemble models outperform single model uncertainties and predictions for operator-learning of hypersonic flows [43.148818844265236]
  Training scientific machine learning (SciML) models on limited high-fidelity data offers one approach to rapidly predict behaviors for situations that have not been seen before. High-fidelity data is itself in limited quantity to validate all outputs of the SciML model in unexplored input space. We extend a DeepONet using three different uncertainty mechanisms: mean-variance estimation, evidential uncertainty, and ensembling.
  arXiv  Detail & Related papers  (2023-10-31T18:07:29Z)
• Particle clustering in turbulence: Prediction of spatial and statistical properties with deep learning [6.91821181311687]
  We simulate the dynamics of particles in the Epstein drag regime within a periodic domain of isotropic forced hydrodynamic turbulence. We train a U-Net deep learning model to predict gridded representations of the particle density and velocity fields, given as input the corresponding fluid fields. Our results suggest that, given appropriately expanded training data, deep learning could complement direct numerical simulations in predicting particle clustering within turbulent flows.
  arXiv  Detail & Related papers  (2022-10-05T15:46:28Z)

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.