用图神经网络预测机翼气压分布，速度快精度高。
Generative Spatio-temporal GraphNet for Transonic Wing Pressure Distribution Forecasting
- 用自编码器压缩数据，图卷积处理非结构化网格，时序层捕捉变化规律。
- 在超临界机翼测试中，预测精度接近计算流体力学，速度更快。
- 适合需要快速模拟气动特性的飞行器设计与实时控制场景。
本研究提出一种框架，用于预测非定常超临界机翼气压分布。该框架结合自编码器、图卷积网络和基于图的时序层，以建模时间依赖性。自编码器将高维气压数据压缩至低维隐空间，实现高效表示并保留关键特征。在隐空间中，基于图的时序层利用历史数据预测未来气压，有效捕捉时间动态，提升预测精度。该方法融合自编码器的降维能力、图卷积对非结构化网格的处理优势以及时序层对序列变化的建模能力。通过在基准超临界机翼测试案例上的验证，其预测精度接近计算流体力学（CFD），同时显著降低预测时间。该框架为非定常气动现象的分析提供了可扩展、计算高效的解决方案。
原文摘要 · Abstract (English)
This study presents a framework for predicting unsteady transonic wing pressure distributions, integrating an autoencoder architecture with graph convolutional networks and graph-based temporal layers to model time dependencies. The framework compresses high-dimensional pressure distribution data into a lower-dimensional latent space using an autoencoder, ensuring efficient data representation while preserving essential features. Within this latent space, graph-based temporal layers are employed to predict future wing pressures based on past data, effectively capturing temporal dependencies and improving predictive accuracy. This combined approach leverages the strengths of autoencoders for dimensionality reduction, graph convolutional networks for handling unstructured grid data, and temporal layers for modeling time-based sequences. The effectiveness of the proposed framework is validated through its application to the Benchmark Super Critical Wing test case, achieving accuracy comparable to computational fluid dynamics, while significantly reducing prediction time. This framework offers a scalable, computationally efficient solution for the aerodynamic analysis of unsteady phenomena.
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