Deformation Behavior

Deformation behavior research focuses on understanding and modeling how objects change shape under various forces, encompassing both rigid and non-rigid materials. Current research emphasizes data-driven approaches, employing neural networks (including transformers, UNets, and graph neural networks) to learn complex deformation patterns from experimental data or simulations, often incorporating physics-based constraints for improved accuracy. This field is crucial for advancing robotics (especially in manipulation of deformable objects), computer graphics (shape completion and editing), and various engineering applications (e.g., structural analysis, material science) by enabling more accurate modeling and prediction of deformation processes.