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This study presents the first on-site validation of a predictive collective control framework that integrates a graph neural network (GNN)\u2013based surrogate model with reinforcement learning (RL). Using a GNN surrogate trained on 9229 high-fidelity simulation samples, a proximal policy optimization agent infers opening angles for adaptive solar control across thousands of interconnected units. Summer-solstice stadium simulations show that, compared with an independently controlled multilayer perceptron\u2013RL baseline, the proposed GNN\u2013RL framework reduces spectator heat load by 11.05% at peak solar conditions (14:00) while increasing field solar exposure by 20.63% at low-load periods (16:00), reflecting a time-dependent Pareto trade-off between spectator comfort and turf daylighting. Crucially, the learned policy is deployed directly to physical hardware\u2014without policy transfer\u2014so a 1:30 mock-up reproduces emergent behaviors from global graph interactions. On-site experiments achieve a 10.3% reduction in spectator solar heat gain and a 25.4% decrease in actuation effort, demonstrating stable and energy-efficient real-time automation.<\/jats:p>","DOI":"10.1093\/jcde\/qwag038","type":"journal-article","created":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T11:44:45Z","timestamp":1776253485000},"page":"1-30","source":"Crossref","is-referenced-by-count":0,"title":["Predictive collective control of large-scale kinetic facades via graph neural networks and reinforcement learning: From simulation to physical validation"],"prefix":"10.1093","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-6350-5094","authenticated-orcid":false,"given":"Soochul","family":"Shin","sequence":"first","affiliation":[{"name":"The Graduate School of Advanced Imaging Science, Multimedia & Film, Chung-Ang University , 84 Heukseok-ro, Seoul 06974 ,","place":["Republic of 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