Cavity Expansion

Cavity expansion, the study of the stress and deformation around a void expanding within a material, is a fundamental problem across diverse fields, from geotechnical engineering to protein structure analysis and accelerator physics. Current research focuses on developing and applying advanced computational methods, such as physics-informed neural networks (PINNs) and machine learning algorithms like DBSCAN, to efficiently and accurately model cavity expansion in complex scenarios, including those involving non-linear material behavior and irregular geometries. These advancements improve predictions of cavity formation and evolution, leading to better designs in engineering applications and enhanced understanding of processes in biological systems and particle accelerators.