Strain Map

Strain mapping aims to visualize and quantify the deformation within materials at various scales, from atomic lattices to macroscopic structures, providing crucial insights into material properties and behavior. Current research focuses on developing advanced computational methods, including deep learning architectures like U-Nets and convolutional neural networks, and physics-informed neural networks, to efficiently and accurately generate strain maps from diverse data sources such as electron microscopy images and finite element simulations. These techniques are improving the analysis of materials with complex microstructures and enabling applications in diverse fields, including materials science, biomechanics (e.g., cardiovascular health), and advanced manufacturing through improved strain engineering.