arXiv：cs.AI· Shaofan Wang, Philipp Geyer·· 16 小时前
基于画布与坐标的几何条件神经网络在变房间尺寸室内环境实时预测中的对比研究
Comparative Study of Canvas- and Coordinate-Based Geometry-Conditioned Neural Networks for Real-Time Prediction of Indoor Environments with Varying Room Dimensions
AI 导读
该研究对比了画布式神经网络（CaNN）与坐标式神经网络（CoNN）两种几何条件代理模型，用于不同房间尺寸下的室内气流与温度场快速预测。CaNN 在固定画布中保留不同占用区域以显式编码房间尺寸，CoNN 则使用归一化坐标通道，两者均采用 U-Net 自编码器分别预测气流与温度场。在高度和长度变化的简化房间基准上，CaNN 与 CFD 结果的一致性高于 CoNN。
正文
Abstract:Computational fluid dynamics (CFD) is computationally expensive for iterative building design. Deep learning surrogates offer faster predictions, but generalization across varying room dimensions remains challenging. This study compares two geometry-conditioned neural network surrogate models for rapid indoor airflow and temperature field prediction under varying room sizes. During the architectural design stage, this method is relevant for real-time assessment of whether conditioned supply air will effectively provide fresh and cool air throughout rooms with varying heights and lengths. The canvas-based neural network (CaNN) explicitly encodes room size by preserving different occupied regions within a fixed canvas, whereas the coordinate-based neural network (CoNN) uses normalized coordinate channels. Both employ U-Net autoencoders to separately predict airflow and temperature fields. Evaluation uses simplified rooms with varying heights and lengths as a controlled benchmark. The results show that CaNN achieves higher agreement with CFD results than CoNN, demonstrating its potential to support performance-driven building design.
| Subjects: | Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.15885 [cs.AI] |
| (or arXiv:2610.15885v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.15885 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Shaofan Wang [view email]
[v1]
Sun, 11 Oct 2026 20:03:49 UTC (1,786 KB)
来源：arXiv：cs.AI · arxiv.org