Quantitative framework for causality evaluation in data-driven model:A case study of a cooling tower systemOA
Quantitative framework for causality evaluation in data-driven model:A case study of a cooling tower system
Jin Hong Kim;Chul Hong Park;Seon Young Heo;Cheol Soo Park
Department of Architecture and Architectural Engineering,College of Engineering,Seoul National University,1,Gwanak-ro,Gwanak-gu,Seoul,08826,Republic of KoreaDepartment of Architecture and Architectural Engineering,College of Engineering,Seoul National University,1,Gwanak-ro,Gwanak-gu,Seoul,08826,Republic of KoreaDepartment of Architecture and Architectural Engineering,College of Engineering,Seoul National University,1,Gwanak-ro,Gwanak-gu,Seoul,08826,Republic of KoreaDepartment of Architecture and Architectural Engineering,Institute of Construction and Environmental Engineering,Institute of Engineering Research,College of Engineering,Seoul National University,1,Gwanak-ro,Gwanak-gu,Seoul,08826,Republic of Korea
causality quantificationcooling towerrepresentational fidelitydynamic fidelityphysics-based modelartificial neural networktransfer learning
causality quantificationcooling towerrepresentational fidelitydynamic fidelityphysics-based modelartificial neural networktransfer learning
《建筑模拟（英文版）》 2026 (4)
903-919,17
This research was sponsored by the Samsung C&T research grant program.Open Access funding enabled and organized by Seoul National University.
评论