3D Rigid

3D rigid body dynamics research focuses on accurately modeling and predicting the motion of three-dimensional objects, encompassing both translational and rotational movements under various forces and interactions. Current efforts leverage deep learning architectures, such as deep residual networks and physics-informed neural networks, often employing novel representations like dual quaternions to efficiently capture complex interactions and improve prediction accuracy. This research is significant for advancing fields like robotics, computer vision, and animation, enabling more realistic simulations and improved control of physical systems. Furthermore, unsupervised learning techniques are being explored to improve the efficiency and robustness of these models.