Accurate and efficient prediction of aeroengine performance is of paramount importance for engine design, maintenance, and optimization endeavours. However, existing methodologies often struggle to strike an optimal balance among predictive accuracy, computational efficiency, modelling complexity, and data dependency. To address these challenges, we propose a strategy that synergistically combines domain knowledge from both the aeroengine and neural network realms to enable real-time prediction of engine performance parameters. Leveraging aeroengine domain knowledge, we judiciously design the network structure and regulate the internal information flow. Concurrently, drawing upon neural network domain expertise, we devise four distinct feature fusion methods and introduce an innovative loss function formulation. To rigorously evaluate the effectiveness and robustness of our proposed strategy, we conduct comprehensive validation across two distinct datasets. The empirical results demonstrate :(1) the evident advantages of our tailored loss function; (2) our model's ability to maintain equal or superior performance with a reduced parameter count; (3) our model's reduced data dependency compared to generalized neural network architectures; (4)Our model is more interpretable than traditional black box machine learning methods.

通过结合飞机引擎领域和神经网络领域的领域知识，我们提出了一种策略，能够实时预测引擎性能参数，并且在预测准确性、计算效率、建模复杂性和数据依赖性之间取得理想平衡。我们通过精心设计网络结构、调节内部信息流，并采用四种不同的特征融合方法和创新的损失函数公式来实现这一目标。通过对两个不同数据集的全面验证，实验证明了我们的策略的有效性和健壮性，并且相较于传统的黑盒机器学习方法，我们的模型具有更好的解释性。

使用物理内嵌数据驱动方法预测航空发动机性能