ArticleScientific reports2020
Predicting porosity, permeability, and tortuosity of porous media from images by deep learning.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
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- Advanced stacked modeling techniques for material porosity estimation via high-resolution computed tomography imaging.Scientific reports · 2026Article
- A hybrid deep boosting framework with adaptive label stabilization for SEM-based porosity estimation in fly-ash cement mortar.Frontiers in artificial intelligence · 2026Article
- Application of stacked bidirectional LSTM neural networks in reservoir porosity prediction.Scientific reports · 2025Article
- Machine Learning for Thermal Transport Prediction in Nanoporous Materials: Progress, Challenges, and Opportunities.Nanomaterials (Basel, Switzerland) · 2025Review
- Assessing proxy and AI models performance in waterflooding optimization.Scientific reports · 2025Article
- Structure and Swelling Properties of Biodegradable Cross-Linked Polyurethanes by Means of Nuclear Magnetic Resonance.The journal of physical chemistry. B · 2025Article
- Fracture Behavior and Toughness Evaluation of Shotcrete: A Closed-Form Approach.Materials (Basel, Switzerland) · 2025Article
- Formation permeability estimation using mud loss data by deep learning.Scientific reports · 2025Article
- 3D microstructure reconstruction and characterization of porous materials using a cross-sectional SEM image and deep learning.Heliyon · 2024Article
- Comprehensive input models and machine learning methods to improve permeability prediction.Scientific reports · 2024Article
- Deep learning for diffusion in porous media.Scientific reports · 2023Article
- Light distribution in fat cell layers at physiological temperatures.Scientific reports · 2023Article
- Inverse design of anisotropic spinodoid materials with prescribed diffusivity.Scientific reports · 2022Article
- Optical clearing and testing of lung tissue using inhalation aerosols: prospects for monitoring the action of viral infections.Biophysical reviews · 2022Review
- Self-normalized density map (SNDM) for counting microbiological objects.Scientific reports · 2022Article
- Influence of the parameters of the convolutional neural network model in predicting the effective compressive modulus of porous structure.Frontiers in bioengineering and biotechnology · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Convolutional neural networks (CNN) are utilized to encode the relation between initial configurations of obstacles and three fundamental quantities in porous media: porosity ([Formula: see text]), permeability (k), and tortuosity (T). The two-dimensional systems with obstacles are considered. The fluid flow through a porous medium is simulated with the lattice Boltzmann method. The analysis has been performed for the systems with [Formula: see text] which covers five orders of magnitude a span for permeability [Formula: see text] and tortuosity [Formula: see text]. It is shown that the CNNs can be used to predict the porosity, permeability, and tortuosity with good accuracy. With the usage of the CNN models, the relation between T and [Formula: see text] has been obtained and compared with the empirical estimate.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.