GPU-Accelerated Simulation of Densely Connected Tensegrity Networks for Statistical Analysis of Morphological Convergence

Densely connected tensegrity networks provide a useful testbed for morphology-driven dynamics, but their quadratic cable interactions make large-scale, long-horizon simulation prohibitively expensive for CPU-based implementations, limiting quantitative studies of morphological properties. This study presents a size-scalable dynamic model and a GPU-accelerated simulator for two-dimensional multi-rod tensegrity systems under dense connectivity, while preserving the same tension-only cable mechanics and fixed-step time integration scheme as a CPU baseline. Leveraging the resulting throughput, we conduct Monte Carlo sweeps over system size and randomized initial conditions, and quantify morphological convergence to a conservative feasibility set using simple, interpretable scalar metrics. Across the tested range, larger networks exhibit reduced variability in morphology statistics, higher convergence rates, and typically shorter convergence times. These results provide quantitative evidence that strong internal coupling supports statistically robust morphological convergence, and show that GPU acceleration enables reproducible, large-scale evaluation of morphology-driven dynamics in densely connected tensegrity networks.