Integrating Symbolic Neural Networks with Building Physics: A Study and Proposal
It addresses inverse problems in building physics for energy efficiency and sustainability, but is incremental as it applies an existing method to a new domain.
This study applied Kolmogorov-Arnold Networks (KAN) to building physics, demonstrating their ability to rediscover fundamental equations, approximate complex formulas, and capture time-dependent heat transfer dynamics through four case studies.
Symbolic neural networks, such as Kolmogorov-Arnold Networks (KAN), offer a promising approach for integrating prior knowledge with data-driven methods, making them valuable for addressing inverse problems in scientific and engineering domains. This study explores the application of KAN in building physics, focusing on predictive modeling, knowledge discovery, and continuous learning. Through four case studies, we demonstrate KAN's ability to rediscover fundamental equations, approximate complex formulas, and capture time-dependent dynamics in heat transfer. While there are challenges in extrapolation and interpretability, we highlight KAN's potential to combine advanced modeling methods for knowledge augmentation, which benefits energy efficiency, system optimization, and sustainability assessments beyond the personal knowledge constraints of the modelers. Additionally, we propose a model selection decision tree to guide practitioners in appropriate applications for building physics.