Porosity Distribution

Porosity distribution, the spatial arrangement of voids within a material, is crucial for understanding and predicting material properties and behavior across diverse fields. Current research focuses on accurately modeling and predicting porosity distributions using advanced techniques, including multi-continuum models enhanced by machine learning (e.g., neural networks), and deep learning approaches such as convolutional neural networks and generative adversarial networks. These methods aim to improve the accuracy and efficiency of porosity estimation from various data sources, such as images of porous materials and seismic data, impacting fields ranging from additive manufacturing and material science to reservoir characterization and geophysics.