Learning Pore-scale Multiphase Flow from 4D Velocimetry

• URL: http://arxiv.org/abs/2603.12516v1
• Date: Thu, 12 Mar 2026 23:28:20 GMT
• Title: Learning Pore-scale Multiphase Flow from 4D Velocimetry
• Abstract summary: Multiphase flow in porous media underpins subsurface energy and environmental technologies.<n>We introduce a framework that infers multiphase pore-scale flow directly from time-resolved micro-velocimetry measurements.
• Score: 21.658780881627536
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multiphase flow in porous media underpins subsurface energy and environmental technologies, including geological CO$_2$ storage and underground hydrogen storage, yet pore-scale dynamics in realistic three-dimensional materials remain difficult to characterize and predict. Here we introduce a multimodal learning framework that infers multiphase pore-scale flow directly from time-resolved four-dimensional (4D) micro-velocimetry measurements. The model couples a graph network simulator for Lagrangian tracer-particle motion with a 3D U-Net for voxelized interface evolution. The imaged pore geometry serves as a boundary constraint to the flow velocity and the multiphase interface predictions, which are coupled and updated iteratively at each time step. Trained autoregressively on experimental sequences in capillary-dominated conditions ($Ca\approx10^{-6}$), the learned surrogate captures transient, nonlocal flow perturbations and abrupt interface rearrangements (Haines jumps) over rollouts spanning seconds of physical time, while reducing hour-to-day--scale direct numerical simulations to seconds of inference. By providing rapid, experimentally informed predictions, the framework opens a route to ''digital experiments'' to replicate pore-scale physics observed in multiphase flow experiments, offering an efficient tool for exploring injection conditions and pore-geometry effects relevant to subsurface carbon and hydrogen storage.

Related papers

• ShipNet: A Geometric Deep Learning Surrogate for Real-Time Ship Hydrodynamics [0.0]
  ShipNet is a geometric deep-learning surrogate that predicts both hull-surface pressure distributions and far-field free-surface wave patterns directly from hull geometry and speed.<n>Inference requires approximately 0.15s per case, yielding over a 550x speedup relative to the potential-flow solver on conventional hardware.
  arXiv  Detail & Related papers  (2026-06-13T15:38:24Z)
• Anisotropic Permeability Tensor Prediction from Porous Media Microstructure via Physics-Informed Progressive Transfer Learning with Hybrid CNN-Transformer [0.0]
  Prediction of permeability tensors from pore-scale microstructure images is essential for subsurface flow modeling, yet direct numerical simulation requires hours per sample.<n>A physics-informed deep learning framework is presented that resolves this bottleneck by combining a MaxViT hybrid CNN-Transformer architecture with progressive transfer learning and differentiable physical constraints.
  arXiv  Detail & Related papers  (2026-03-18T09:41:01Z)
• Ada3Drift: Adaptive Training-Time Drifting for One-Step 3D Visuomotor Robotic Manipulation [53.750389076941396]
  Diffusion-based visuomotor policies capture multimodal action distributions through iterative denoising, but their high inference latency limits real-time robotic control.<n>We propose Ada3Drift, which learns a training-time drifting field that attracts predicted actions toward expert demonstration modes.<n>Ada3Drift achieves state-of-the-art performance while requiring $10times$ fewer function evaluations than diffusion-based alternatives.
  arXiv  Detail & Related papers  (2026-03-12T14:36:53Z)
• SurfPhase: 3D Interfacial Dynamics in Two-Phase Flows from Sparse Videos [37.70003012602796]
  We propose SurfPhase, a novel model for reconstructing 3D interfacial dynamics from sparse camera views.<n>We evaluate on a new dataset of high-speed pool boiling videos, demonstrating high-quality view synthesis and velocity estimation from only two camera views.
  arXiv  Detail & Related papers  (2026-02-11T18:59:55Z)
• EMoTive: Event-guided Trajectory Modeling for 3D Motion Estimation [59.33052312107478]
  Event cameras offer possibilities for 3D motion estimation through continuous adaptive pixel-level responses to scene changes.<n>This paper presents EMove, a novel event-based framework that models-uniform trajectories via event-guided parametric curves.<n>For motion representation, we introduce a density-aware adaptation mechanism to fuse spatial and temporal features under event guidance.<n>The final 3D motion estimation is achieved through multi-temporal sampling of parametric trajectories, flows and depth motion fields.
  arXiv  Detail & Related papers  (2025-03-14T13:15:54Z)
• Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network [2.2101344151283944]
  Current numerical models are often incapable of accurately capturing the complex pore-scale physics observed in experiments. We propose a graph neural network-based approach and directly learn pore-scale fluid flow using micro-CT experimental data.
  arXiv  Detail & Related papers  (2024-11-21T15:01:17Z)
• 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)
• Correlating sparse sensing for large-scale traffic speed estimation: A Laplacian-enhanced low-rank tensor kriging approach [76.45949280328838]
  We propose a Laplacian enhanced low-rank tensor (LETC) framework featuring both lowrankness and multi-temporal correlations for large-scale traffic speed kriging. We then design an efficient solution algorithm via several effective numeric techniques to scale up the proposed model to network-wide kriging.
  arXiv  Detail & Related papers  (2022-10-21T07:25:57Z)
• Towards Fast Simulation of Environmental Fluid Mechanics with Multi-Scale Graph Neural Networks [0.0]
  We introduce MultiScaleGNN, a novel multi-scale graph neural network model for learning to infer unsteady continuum mechanics. We demonstrate this method on advection problems and incompressible fluid dynamics, both fundamental phenomena in oceanic and atmospheric processes. Simulations obtained with MultiScaleGNN are between two and four orders of magnitude faster than those on which it was trained.
  arXiv  Detail & Related papers  (2022-05-05T13:33:03Z)
• A Physics-Constrained Deep Learning Model for Simulating Multiphase Flow in 3D Heterogeneous Porous Media [1.4050836886292868]
  A physics-constrained deep learning model is developed for solving multiphase flow in 3D heterogeneous porous media. The model is trained from physics-based simulation data and emulates the physics process. The model performs prediction with a speedup of 1400 times compared to physics-based simulations.
  arXiv  Detail & Related papers  (2021-04-30T02:15:01Z)
• Machine learning for rapid discovery of laminar flow channel wall modifications that enhance heat transfer [56.34005280792013]
  We present a combination of accurate numerical simulations of arbitrary, flat, and non-flat channels and machine learning models predicting drag coefficient and Stanton number. We show that convolutional neural networks (CNN) can accurately predict the target properties at a fraction of the time of numerical simulations.
  arXiv  Detail & Related papers  (2021-01-19T16:14:02Z)
• Multi-view Depth Estimation using Epipolar Spatio-Temporal Networks [87.50632573601283]
  We present a novel method for multi-view depth estimation from a single video. Our method achieves temporally coherent depth estimation results by using a novel Epipolar Spatio-Temporal (EST) transformer. To reduce the computational cost, inspired by recent Mixture-of-Experts models, we design a compact hybrid network.
  arXiv  Detail & Related papers  (2020-11-26T04:04:21Z)
• ML-LBM: Machine Learning Aided Flow Simulation in Porous Media [0.0]
  Direct simulation of fluid flow in porous media requires significant computational resources to solve within reasonable timeframes. An integrated method combining predictions of fluid flow with direct flow simulation is outlined. Deep Learning techniques based on Convolutional Neural Networks (CNNs) are shown to give an accurate estimate of the steady state velocity fields.
  arXiv  Detail & Related papers  (2020-04-22T01:55:59Z)

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.