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.

翻译：飞行器设计优化传统上依赖于计算成本高昂的仿真技术，如有限元法（FEM）和有限体积法（FVM）。这些方法虽然精确，但会显著拖慢设计迭代过程。核心挑战在于降低计算复杂度，同时保持高精度，以便快速评估多种设计方案。本研究探索了先进方法，包括代理模型、降阶模型（ROM）以及多保真度机器学习技术，以实现更高效的飞行器设计评估。具体而言，该研究探讨了多保真度物理信息神经网络（MPINN）与用于流形对齐的自编码器的应用，以及生成对抗网络（GANs）在优化设计几何形状方面的潜力。通过一个概念验证任务，研究证明了从低保真度仿真预测高保真度结果的能力，为更快、更具成本效益的飞行器设计迭代提供了一条路径。