GAESA-iFormer: a graph attention-enhanced scale-aware inverted transformer for performance characterization of aircraft engine compressors

Abstract

Accurate determination of aircraft engine compressor characteristics is vital for stability and performance optimization. However, experimental data acquisition is often hindered by high costs, resulting in a strong reliance on limited test data. To address this challenge, this paper proposes the Graph Attention Enhanced Scale-Aware Inverted Transformer (GAESA-iFormer), a novel surrogate modeling framework integrating a Graph Attention Network (GAT) encoder, a Multi-scale Feature Enhancement (MSFE) module, and a cross-attention decoder based on the inverted Transformer architecture. The GAT encoder explicitly models relational dependencies among neighboring operating points along each speed line, capturing local continuity while reducing computational complexity. The MSFE module employs parallel convolutional kernels of sizes 1, 3, and 5 to extract multi-granularity features, effectively broadening the receptive field. Furthermore, the cross-attention decoder enables explicit knowledge transfer across different speed lines, leveraging structural similarities between well-sampled and sparsely sampled speeds—a critical advantage when data is scarce. Comprehensive evaluations demonstrate that GAESA-iFormer achieves optimal performance with a feature dimension of 64 and four encoder layers. When trained on transformed secondary data, the proposed model significantly outperforms state-of-the-art baselines, reducing the RMSE by 27.03% and MAE by 31.78%. The results indicate that the proposed GAESA-iFormer model achieves improved prediction accuracy on the current compressor dataset under limited experimental data.