arXiv：cs.LG（机器学习，全量分类）· Qineng Wang, Xinrui Zhou, Shuwen Yue, Kangli Bao, Hairun Xie, Yonghe Zhang·· 14 小时前AI 评分32
TFCN：面向组件化航天系统物理场预测的组合式嵌入架构
Compositional Embedding Architecture for Physical Field Prediction in Componentized Aerospace Systems
AI 导读
研究者提出树结构因子组合网络（TFCN），将航天器热配置分解为可复用的局部物理因子，用树结构组合模块学习不同因子组合对应的全局温度场响应。模型仅在不超过15个发热组件的配置上训练，在16-25个组件的分布外配置上评测，定温与辐射通量两种边界条件下RMSE分别为4.21 K和18.62 K，较最强基线降低65.6%和33.6%。
正文
Abstract:Spacecraft thermal design requires repeated evaluation of how variations in the number and spatial arrangement of heat-generating components and in thermal boundary conditions affect the temperature field. High-fidelity numerical simulations are computationally expensive and therefore difficult to use for large-scale design screening. Although surrogate models can accelerate temperature-field prediction, existing approaches generally encode each complete configuration as a whole and do not explicitly exploit the reusability of local physical constituents across configurations, which limits their accuracy for component counts and combinations not covered during training. To address this issue, we propose the Tree-Structured Factor Composition Network (TFCN), which decomposes complex spacecraft thermal configurations into reusable local physical factors and employs a tree-structured composition module to learn the global temperature-field response associated with different factor combinations. TFCN is evaluated on two-dimensional steady-state spacecraft thermal-analysis cases with prescribed-temperature and radiative-flux boundary conditions. The model is trained exclusively on configurations containing no more than 15 heat-generating components and evaluated on unseen configurations containing 16-25 components. For the prescribed-temperature and radiative-flux cases, TFCN achieves component-count out-of-distribution RMSE values of 4.21 K and 18.62 K, respectively, representing reductions of 65.6% and 33.6% relative to the strongest baseline. These results demonstrate that TFCN improves the reliability of temperature-field prediction under variations in component count and provides an efficient surrogate for rapid spacecraft thermal-design evaluation and large-scale configuration screening.
| Comments: | 32 pages, 13 figures |
| Subjects: | Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.00237 [cs.CE] |
| (or arXiv:2610.00237v1 [cs.CE] for this version) | |
| https://doi.org/10.48550/arXiv.2610.00237 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Qineng Wang [view email]
[v1]
Wed, 23 Sep 2026 04:15:34 UTC (6,825 KB)
来源：arXiv：cs.LG（机器学习，全量分类） · arxiv.org