Pore Space

Pore space analysis focuses on characterizing the distribution and properties of voids within materials, crucial for understanding diverse phenomena from fluid transport in rocks to material defects in manufactured parts. Current research heavily utilizes machine learning, particularly deep learning architectures like convolutional neural networks (CNNs), generative adversarial networks (GANs), and recurrent neural networks (RNNs, such as LSTMs), to analyze complex pore geometries from imaging data (e.g., micro-CT scans) and predict material properties. These advancements improve the accuracy and efficiency of pore space characterization, impacting fields ranging from materials science and petroleum engineering to soil science and biomedical applications. The integration of physics-informed models with machine learning further enhances the reliability and interpretability of predictions.