论文标题
物理信息MTA-UNET：卫星热应力和热变形的预测
Physics-informed MTA-UNet: Prediction of Thermal Stress and Thermal Deformation of Satellites
论文作者
论文摘要
热应力和变形的快速分析在热控制措施和卫星结构设计的优化中起关键作用。为了实现卫星主板的实时热应力和热变形分析，本文提出了一种新型的多任务注意UNET（MTA-UNET）神经网络，该神经网络结合了多任务学习（MTL）和U-NET的优势与注意机制。此外，在训练过程中使用了一种物理知识的策略，其中部分微分方程（PDE）被整合到损失函数中作为残留术语。最后，将基于不确定性的损失平衡方法应用于重量的多个培训任务的不同损失功能。实验结果表明，与单任务学习（STL）模型相比，提出的MTA-UNET有效提高了多个物理任务的预测准确性。此外，物理信息的方法在每个任务的预测中的错误较小，尤其是在小型数据集上。代码可以在：\ url {https://github.com/komorebitso/mta-unet}下载。
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}.
