Force Displacement

Force-displacement modeling focuses on understanding and predicting the relationship between applied forces and resulting deformations in materials and structures, particularly in scenarios involving nonlinear behavior. Current research emphasizes developing accurate and efficient models, employing techniques like neural networks (including physically guided networks), co-rotational modeling, and probabilistic deep learning approaches to capture complex material responses and improve prediction accuracy. This work is crucial for advancing robotics (e.g., soft grippers and legged locomotion on uneven terrain), optimizing 3D-printed structures, and enabling real-time simulations for applications such as computer-aided surgery. The development of robust and computationally efficient models is driving progress across diverse fields.