PoreDiT: A Scalable Generative Model for Large-Scale Digital Rock Reconstruction

• URL: http://arxiv.org/abs/2604.10171v1
• Date: Sat, 11 Apr 2026 11:39:29 GMT
• Title: PoreDiT: A Scalable Generative Model for Large-Scale Digital Rock Reconstruction
• Abstract summary: PoreDiT is a novel generative model designed for high-efficiency digital rock reconstruction at gigavoxel scales.<n>By directly predicting the binary probability field of pore spaces, the model preserves key topological features critical for pore-scale fluid flow and transport simulations.
• Score: 3.1899541701942002
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This manuscript presents PoreDiT, a novel generative model designed for high-efficiency digital rock reconstruction at gigavoxel scales. Addressing the significant challenges in digital rock physics (DRP), particularly the trade-off between resolution and field-of-view (FOV), and the computational bottlenecks associated with traditional deep learning architectures, PoreDiT leverages a three-dimensional (3D) Swin Transformer to break through these limitations. By directly predicting the binary probability field of pore spaces instead of grayscale intensities, the model preserves key topological features critical for pore-scale fluid flow and transport simulations. This approach enhances computational efficiency, enabling the generation of ultra-large-scale ($1024^3$ voxels) digital rock samples on consumer-grade hardware. Furthermore, PoreDiT achieves physical fidelity comparable to previous state-of-the-art methods, including accurate porosity, pore-scale permeability, and Euler characteristics. The model's ability to scale efficiently opens new avenues for large-domain hydrodynamic simulations and provides practical solutions for researchers in pore-scale fluid mechanics, reservoir characterization, and carbon sequestration.

Related papers

• 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)
• Transolver-3: Scaling Up Transformer Solvers to Industrial-Scale Geometries [51.028432812178266]
  Transolver-3 is a new member of the Transolver family designed for high-fidelity physics simulations.<n>We show that Transolver-3 is capable of handling meshes with over 160 million cells, achieving impressive performance across three challenging simulation benchmarks.
  arXiv  Detail & Related papers  (2026-02-04T16:52:44Z)
• Controlled Latent Diffusion Models for 3D Porous Media Reconstruction [2.61662361742721]
  Three-dimensional digital reconstruction of porous media presents a fundamental challenge in geoscience.<n>We introduce a computational framework that addresses this challenge through latent diffusion models operating within the EDM framework.<n>Our approach reduces dimensionality via a custom variational autoencoder trained in binary volumes, improving efficiency and also enabling the generation of larger volumes.
  arXiv  Detail & Related papers  (2025-03-31T13:36:55Z)
• SparseFlex: High-Resolution and Arbitrary-Topology 3D Shape Modeling [79.56581753856452]
  SparseFlex is a novel sparse-structured isosurface representation that enables differentiable mesh reconstruction at resolutions up to $10243$ directly from rendering losses.<n>By enabling high-resolution, differentiable mesh reconstruction and generation with rendering losses, SparseFlex significantly advances the state-of-the-art in 3D shape representation and modeling.
  arXiv  Detail & Related papers  (2025-03-27T17:46:42Z)
• Fourier Neural Operator based surrogates for $CO_2$ storage in realistic geologies [57.23978190717341]
  We develop a Neural Operator (FNO) based model for real-time, high-resolution simulation of $CO$ plume migration.<n>The model is trained on a comprehensive dataset generated from realistic subsurface parameters.<n>We present various strategies for improving the reliability of predictions from the model, which is crucial while assessing actual geological sites.
  arXiv  Detail & Related papers  (2025-03-14T02:58:24Z)
• Constrained Transformer-Based Porous Media Generation to Spatial Distribution of Rock Properties [0.0]
  Pore-scale modeling of rock images based on information in 3D micro-computed tomography data is crucial for studying complex subsurface processes. We propose a two-stage modeling framework that combines a Vector Quantized Variational Autoencoder (VQVAE) and a transformer model for spatial upscaling and arbitrary-size 3D porous media reconstruction.
  arXiv  Detail & Related papers  (2024-10-28T19:03:33Z)
• Pixelated Reconstruction of Foreground Density and Background Surface Brightness in Gravitational Lensing Systems using Recurrent Inference Machines [116.33694183176617]
  We use a neural network based on the Recurrent Inference Machine to reconstruct an undistorted image of the background source and the lens mass density distribution as pixelated maps. When compared to more traditional parametric models, the proposed method is significantly more expressive and can reconstruct complex mass distributions.
  arXiv  Detail & Related papers  (2023-01-10T19:00:12Z)
• Learning Large-scale Subsurface Simulations with a Hybrid Graph Network Simulator [57.57321628587564]
  We introduce Hybrid Graph Network Simulator (HGNS) for learning reservoir simulations of 3D subsurface fluid flows. HGNS consists of a subsurface graph neural network (SGNN) to model the evolution of fluid flows, and a 3D-U-Net to model the evolution of pressure. Using an industry-standard subsurface flow dataset (SPE-10) with 1.1 million cells, we demonstrate that HGNS is able to reduce the inference time up to 18 times compared to standard subsurface simulators.
  arXiv  Detail & Related papers  (2022-06-15T17:29:57Z)
• Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling [86.9726984929758]
  We focus on the integration of incomplete physics models into deep generative models. We propose a VAE architecture in which a part of the latent space is grounded by physics. We demonstrate generative performance improvements over a set of synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2021-02-25T20:28:52Z)
• Multi-Scale Neural Networks for to Fluid Flow in 3D Porous Media [0.0]
  We develop a general multiscale deep learning model that is able to learn from porous media simulation data. We enable the evaluation of large images in approximately one second on a single Graphics Processing Unit.
  arXiv  Detail & Related papers  (2021-02-10T23:38:36Z)

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.