  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.
