Rigid Body

Rigid body dynamics studies the motion of objects treated as idealized, undeformable bodies. Current research focuses on improving the efficiency and accuracy of simulating complex rigid body systems, particularly those with many degrees of freedom or intricate interactions, employing methods like deep residual networks, graph neural networks, and advanced Lie group integration schemes. These advancements are crucial for applications ranging from robotics and animation to collision avoidance and the simulation of large-scale scenes, enabling more realistic and computationally efficient modeling of complex physical systems. The development of robust and efficient algorithms for rigid body dynamics is driving progress in various fields that rely on accurate physical simulations.