AI RESEARCH
Symmetry-Reduced Physics-Informed Learning of Tensegrity Dynamics
arXiv CS.LG
•
ArXi:2603.17824v1 Announce Type: new Tensegrity structures possess intrinsic geometric symmetries that govern their dynamic behavior. However, most existing physics-informed neural network (PINN) approaches for tensegrity dynamics do not explicitly exploit these symmetries, leading to high computational complexity and unstable optimization. In this work, we propose a symmetry-reduced physics-informed neural network (SymPINN) framework that embeds group-theory-based symmetry directly into both the solution expression and the neural network architecture to predict tensegrity dynamics.