基于物理学的MTA-UNet：卫星热应力和热变形预测
Physics-informed MTA-UNet: Prediction of Thermal Stress and Thermal Deformation of Satellites
摘要：
热应力和热变形的快速分析对卫星热控措施和结构优化设计具有重要意义。为实现卫星主板热应力和热变形的实时分析，结合多任务学习（MTL）和U-Net的优点，结合注意力机制，提出了一种新的多任务注意力神经网络（MTA-UNet）。此外，在训练过程中使用了物理信息策略，其中偏微分方程（PDE）被集成到损失函数作为残差项。最后，基于不确定性的损失平衡方法被应用于加权多个训练任务的不同损失函数。实验结果表明，与单任务学习（STL）模型相比，MTA-UNet模型有效地提高了多物理任务的预测精度.此外，物理信息方法在预测每个任务时带来的误差较小，特别是在小数据集上。代码可从以下网址下载：\url{https://github.com/KomorebiTso/MTA-UNet}。
英文摘要：
The rapid analysis of thermal stress and deformation plays a pivotal role in the thermal control measures and optimization of the structural design of satellites. For achieving real-time thermal stress and thermal deformation analysis of satellite motherboards, this paper proposes a novel Multi-Task Attention UNet (MTA-UNet) neural network which combines the advantages of both Multi-Task Learning (MTL) and U-Net with attention mechanism. Besides, a physics-informed strategy is used in the training process, where partial differential equations (PDEs) are integrated into the loss functions as residual terms. Finally, an uncertainty-based loss balancing approach is applied to weight different loss functions of multiple training tasks. Experimental results show that the proposed MTA-UNet effectively improves the prediction accuracy of multiple physics tasks compared with Single-Task Learning (STL) models. In addition, the physics-informed method brings less error in the prediction of each task, especially on small data sets. The code can be downloaded at: \url{https://github.com/KomorebiTso/MTA-UNet}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
