[2412.18564] Efficient Aircraft Design Optimization Using Multi-Fidelity Models and Multi-fidelity Physics Informed Neural Networks

Computer Science > Machine Learning arXiv:2412.18564 (cs) [Submitted on 24 Dec 2024 (v1), last revised 28 Feb 2026 (this version, v2)] Title:Efficient Aircraft Design Optimization Using Multi-Fidelity Models and Multi-fidelity Physics Informed Neural Networks Authors:Apurba Sarker View a PDF of the paper titled Efficient Aircraft Design Optimization Using Multi-Fidelity Models and Multi-fidelity Physics Informed Neural Networks, by Apurba Sarker View PDF Abstract: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 ef...