{"slug": "gpu-accelerated-inverse-structural-anastylosis-from-block-collapse-dynamics", "title": "GPU-Accelerated Inverse Structural Anastylosis from Block Collapse Dynamics", "summary": "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.", "body_md": "arXiv:2606.28394v1 Announce Type: new\nAbstract: 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.", "url": "https://wpnews.pro/news/gpu-accelerated-inverse-structural-anastylosis-from-block-collapse-dynamics", "canonical_source": "https://arxiv.org/abs/2606.28394", "published_at": "2026-06-30 04:00:00+00:00", "updated_at": "2026-06-30 04:24:50.428633+00:00", "lang": "en", "topics": ["computer-vision", "machine-learning", "ai-research"], "entities": ["Jenga Inverse Predictor", "ResNet-18", "UNESCO", "Uxmal", "CuPy", "Numba", "Ziglar", "CMU"], "alternates": {"html": "https://wpnews.pro/news/gpu-accelerated-inverse-structural-anastylosis-from-block-collapse-dynamics", "markdown": "https://wpnews.pro/news/gpu-accelerated-inverse-structural-anastylosis-from-block-collapse-dynamics.md", "text": "https://wpnews.pro/news/gpu-accelerated-inverse-structural-anastylosis-from-block-collapse-dynamics.txt", "jsonld": "https://wpnews.pro/news/gpu-accelerated-inverse-structural-anastylosis-from-block-collapse-dynamics.jsonld"}}