대학원소개
논문성과
논문성과
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Journal of Computational Design and Engineering (Prof. Youngho Chai, 1 paper)
관리자 │ 2026-04-20 HIT 473 |
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We are delighted to announce that one paper from the Virtual Environments Lab (VELab, Prof. Youngho Chai) have been accepted to Journal of Computational Design and Engineering. Title: Predictive collective control of large-scale kinetic facades via Graph Neural Networks and Reinforcement Learning: From simulation to physical validation Authors: Soochul Shin, Wonhyung Lee, Youngho Chai Abstract: Real-time coordination of large-scale kinetic facades (KFs) is fundamentally challenged by the need to model strongly coupled module dynamics. This study presents the first on-site validation of a predictive collective control framework that integrates a Graph Neural Network (GNN)–based surrogate model with Reinforcement Learning (RL). Using a GNN surrogate trained on 9 229 high-fidelity simulation samples, a Proximal Policy Optimization (PPO) 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 (MLP)–RL baseline, the proposed GNN–RL 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—without policy transfer—so 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. |
| 이전글 | Pattern Recognition (Prof. Jongwon Choi, 1 paper) |
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| 다음글 | ICPR 2026 (Prof. Jongwon Choi, 4 papers) |
