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<p>Accurate prediction of aircraft engine weight during the early design phase is crucial for aircraft performance evaluation and design decision-making. However, this task remains challenging in resource-constrained engineering settings because available data are often scarce, imbalanced, and characterized by complex interactions among design parameters. To address these challenges, this study proposes a sample-aware regression framework with selective generative data augmentation and graph-based feature learning (SAR-SAGL). First, a selective generative augmentation module identifies hard-to-fit regions and synthesizes local samples to mitigate data scarcity and distribution imbalance. Second, a feature correlation graph is constructed to represent interdependencies among engine design parameters, and a Graph Convolutional Network is used to derive graph-enhanced structural embeddings. Finally, a sample-aware regression mechanism dynamically adjusts sample weights according to fitting errors, enabling the model to focus more on difficult samples during training. Experiments on real-world datasets from NASA and commercial aero-engine projects demonstrate that SAR-SAGL achieves the best overall performance among all compared models. Additional validation and diagnostic analyses further support the effectiveness and applicability of the framework. Overall, this work establishes a promising pathway for sample-efficient regression in data-constrained engineering design.</p>

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Sample-aware regression with selective generative augmentation and graph learning for aircraft engine weight prediction under data scarcity

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月29日

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  • Zhu Xiang,
  • Tianyang Lei,
  • Kewei Yang

摘要

Accurate prediction of aircraft engine weight during the early design phase is crucial for aircraft performance evaluation and design decision-making. However, this task remains challenging in resource-constrained engineering settings because available data are often scarce, imbalanced, and characterized by complex interactions among design parameters. To address these challenges, this study proposes a sample-aware regression framework with selective generative data augmentation and graph-based feature learning (SAR-SAGL). First, a selective generative augmentation module identifies hard-to-fit regions and synthesizes local samples to mitigate data scarcity and distribution imbalance. Second, a feature correlation graph is constructed to represent interdependencies among engine design parameters, and a Graph Convolutional Network is used to derive graph-enhanced structural embeddings. Finally, a sample-aware regression mechanism dynamically adjusts sample weights according to fitting errors, enabling the model to focus more on difficult samples during training. Experiments on real-world datasets from NASA and commercial aero-engine projects demonstrate that SAR-SAGL achieves the best overall performance among all compared models. Additional validation and diagnostic analyses further support the effectiveness and applicability of the framework. Overall, this work establishes a promising pathway for sample-efficient regression in data-constrained engineering design.