用符号神经网络重构建筑物理方程，提升模型可解释性与预测能力
Integrating Symbolic Neural Networks with Building Physics: A Study and Proposal
- 基于KAN网络融合先验物理知识与数据，实现公式自动发现
- 在四个案例中成功还原热传导基本方程并捕捉动态变化规律
- 提出决策树工具，帮助从业者选择适合的建模方法
符号神经网络（如Kolmogorov-Arnold Networks, KAN）为将先验知识与数据驱动方法结合提供了新路径，对科学与工程领域的反问题求解具有重要意义。本研究探讨KAN在建筑物理中的应用，聚焦于预测建模、知识发现与持续学习。通过四个案例研究，证明KAN能够重新发现基础物理方程、近似复杂公式，并捕捉热传递的时间依赖动态。尽管存在外推能力和可解释性方面的挑战，但其在知识增强方面的潜力显著，有助于提升能源效率、系统优化与可持续性评估，突破建模者个人认知局限。此外，本文提出一个模型选择决策树，指导从业者在建筑物理场景中的合理应用。
原文摘要 · Abstract (English)
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.
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