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.

翻译：准确高效地预测航空发动机性能对于发动机设计、维护与优化工作至关重要。然而，现有方法往往难以在预测精度、计算效率、建模复杂度和数据依赖性之间取得最佳平衡。为应对这些挑战，我们提出一种策略，协同融合航空发动机领域与神经网络领域的先验知识，以实现发动机性能参数的实时预测。利用航空发动机领域知识，我们审慎地设计了网络结构并调控内部信息流。同时，借鉴神经网络领域的专业知识，我们设计了四种不同的特征融合方法，并提出了一种创新的损失函数形式。为严格评估所提策略的有效性与鲁棒性，我们在两个不同的数据集上进行了全面验证。实证结果表明：(1) 我们定制的损失函数具有明显优势；(2) 我们的模型能够在减少参数数量的情况下保持同等或更优的性能；(3) 与通用神经网络架构相比，我们的模型降低了数据依赖性；(4) 相较于传统的黑盒机器学习方法，我们的模型具有更好的可解释性。