用低精度模拟快速预测高精度结果，加速飞机设计迭代。
Efficient Aircraft Design Optimization Using Multi-Fidelity Models and Multi-fidelity Physics Informed Neural Networks
- 结合多保真度神经网络与物理约束，从低精度数据推断高精度结果。
- 在概念验证任务中实现低精度仿真到高精度结果的准确预测。
- 适合需要快速评估大量设计方案的航空工程研究人员。
飞机设计优化通常依赖计算成本高昂的仿真技术，如有限元法（FEM）和有限体积法（FVM），虽精度高，但严重拖慢设计迭代速度。本研究探索代理模型、降阶模型（ROM）及多保真度机器学习技术，以降低计算复杂度并保持高精度。具体采用多保真度物理信息神经网络（MPINN）与自编码器进行流形对齐，并探讨生成对抗网络（GANs）在优化设计几何中的潜力。通过概念验证任务，证明可从低保真度仿真有效预测高保真度结果，为更快、更经济的飞机设计迭代提供可行路径。
原文摘要 · Abstract (English)
Aircraft design optimization traditionally relies on computationally expensive simulation techniques such as Finite Element Method (FEM) and Finite Volume Method (FVM), which, while accurate, can significantly slow down the design iteration process. The challenge lies in reducing the computational complexity while maintaining high accuracy for quick evaluations of multiple design alternatives. This research explores advanced methods, including surrogate models, reduced-order models (ROM), and multi-fidelity machine learning techniques, to achieve more efficient aircraft design evaluations. Specifically, the study investigates the application of Multi-fidelity Physics-Informed Neural Networks (MPINN) and autoencoders for manifold alignment, alongside the potential of Generative Adversarial Networks (GANs) for refining design geometries. Through a proof-of-concept task, the research demonstrates the ability to predict high-fidelity results from low-fidelity simulations, offering a path toward faster and more cost effective aircraft design iterations.
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