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MATLAB educational workflow for NDT-based concrete porosity estimation using referenced UPV and electrical resistivity empirical equations, and a GPR attribute-based machine learning demonstration using synthetic data.
This repository contains the source code for the Interaction Topological Transformer (ITT v.1.0.1), a deep learning framework for predicting properties of porous materials, including MOFs, COFs, PPNs, and ZEOs.
Carbon capture and storage (CCS) is an important technology to combat climate change, since it removes carbon dioxide emissions from major industrial sources, helping to reduce greenhouse gas concentrations while the world transitions to cleaner energy systems. In the implementation of CCS projects, petrophysical properties are crucial in determining the most suitable sites to store the CO₂. Therefore, this study applies machine learning techniques to forecast petrophysical properties (density, porosity, and permeability) in the basalts of the Serra Geral Group of the Paraná Basin, located in the State of Santa Catarina, Brazil. By applying m
This release contains the complete Jupyter Notebook–based analysis framework for TPMS scaffold evaluation, porosity–velocity–pressure drop prediction, and machine learning modelling. It includes data preprocessing scripts, ML training pipelines, residual plot generation, permeability estimation, and figure exports used for implant and scaffold simulations. This version represents the first stable, citable release for research and publication purposes.
DPM Tools is a collection of modules for processing and visualization of porous media images. Integrated as workflows, the combination of all module functions provide end-to-end capabilities that streamline pre- and post- processing of porous media images. Individual and combined functions can be used to curate images for publication, prepare images for simulation and machine learning (ML) applications, and analyze simulation and ML results. By design, DPM Tools integrate functions from open Python packages to implement common digital porous media as workflows. DPM Tools is implemented within the Digital Porous Media Portal (formerly Digital
Rapid modelling of reactive transport in porous media using machine learning: limitations and solutions
What's Changed Contains all final files for JOSS submission by @rajatnama in https://github.com/Deep7285/PoroDet/pull/1 update by @rajatnama in https://github.com/Deep7285/PoroDet/pull/3 New Contributors @rajatnama made their first contribution in https://github.com/Deep7285/PoroDet/pull/1 Full Changelog : https://github.com/Deep7285/PoroDet/compare/v1.0.0...v1.1.0 Automation Test *Added automated test suite via pytest.
Title Concrete Image Analyzer (CIA): AI-based Microstructure Characterization Tool for Concrete Materials Summary CIA is an open-source software tool designed for automated microstructural analysis of concrete using classical image processing, machine learning, and computer-vision techniques. It quantifies porosity, ITZ thickness, crack morphology, and aggregate–paste phase distribution without laboratory equipment, enabling scalable digital concrete research. Purpose / Motivation Traditional concrete microstructure studies often require lab setups, microscopy workflows, and manual image interpretation. CIA eliminates this dependency by provi
This repository, "Phase Segmentation of SOC SEM Images using U-Net," provides the complete deep learning pipeline used for semantic segmentation of Solid Oxide Cell (SOC) microstructures from 2D Scanning Electron Microscopy (SEM) images. The release includes: Preprocessing scripts for SEM/EDS image alignment and Ilastik-based ground-truth generation. Dataset preparation pipeline (patch extraction, augmentation, normalization). Training and inference modules implementing a U-Net architecture optimized for four-phase classification: porosity, metallic Ni, NiO, and YSZ. Sliding-window inference utilities with overlap averaging for artifact-free
**# Python Code Modules for Petroleum and Natural Gas Reservoir Analysis** This file contains Python code modules developed for two types of reservoir systems: liquid petroleum and natural gas. Each type is categorized into four typical cases based on porosity level (high/low) and reservoir fluid properties (hydrocarbon-bearing/water-bearing). The code execution specifications are as follows: **### Basic Analysis Modules (Items 1-6, 14-20)** 1.Used for exploring reservoir parameter distribution patterns 2. Each scenario only needs to be executed once **### Scenario Simulation Modules (Items 7-13)** 1. Includes differential simulations for por
The loss of fertile soil due to wind erosion has major impacts on the landscape and the environment. Its intensity and extent depend on many factors, where vegetation cover, soil moisture and wind intensity play key roles. Climatic conditions are constantly developing towards a higher extremity. Episodes of drought associated with a higher risk of wind erosion are longer and more frequent. For farmers, this results in higher demands for management in order to increase resistance against the negative effects of wind erosion. Windbreaks effectively protect the soil by reducing wind speed. This paper describes the automation of the current proce
This repository is created to contain the codes and data used in the research project titled "Transformer-based deep learning models for predicting permeability of porous media".
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