GPU-Accelerated Inverse Structural Anastylosis from Block Collapse Dynamics

Researchers introduced Jenga Inverse Predictor (JIP-2), a GPU-accelerated deep learning framework that reconstructs collapsed stone structures by treating anastylosis as an inverse prediction task. The system uses a physics engine and dual-stream ResNet-18 to predict block removal sequences, with potential applications at the UNESCO Maya site of Uxmal.

arXiv:2606.28394v1 Announce Type: new Abstract: The physical anastylosis of collapsed architectural monuments -- the meticulous reassembly of fallen stone elements into their original structural configuration -- represents one of the most intellectually demanding challenges in conservation science. Traditional approaches depend heavily on expert archaeologist judgement and manual block-by-block correspondence, a process that is both labour-intensive and inherently subjective. Inspired by the combinatorial complexity of this problem as manifested in the game of Jenga, we present Jenga Inverse Predictor , a GPU-accelerated deep learning framework that addresses structural anastylosis as an inverse prediction task. Given an image of a collapsed block assembly, JIP-2 reconstructs the most probable prior tower configuration by: 1 implementing a complete rigid-body physics engine with OBB/SAT collision detection and a Projected Gauss-Seidel PGS contact solver accelerated with Numba JIT and CuPy CUDA; 2 applying the analytical force thresholds of Ziglar CMU, 2006 -- F app = 3 mu s m g Y-axis, torque-free and F app = 4 mu s m g X-axis, torque risk -- over three friction levels mu s in {0.25, 0.40, 0.60} across 450 simulated episodes; 3 training a dual-stream ResNet-18 that injects a friction one-hot vector and jointly predicts block removal count, per-position removal probabilities, centre-of-mass imbalance, and Ziglar torque risk; and 4 generating a smooth 3-D video of the block-by-block reverse reconstruction. We discuss implications for computer-assisted anastylosis at the UNESCO Maya site of Uxmal, Yucatan, and provide a detailed technical description of the full pipeline, architecture, and loss formulation.