智能赋能气动计算与外形设计：途径、前沿与挑战-“AI+空天科学”专刊

doi:10.7527/S1000-6893.2026.33587

Abstract

Abstract: Intelligent technologies, represented by deep neural networks, are driving aerodynamic science and engineering design into a new data-driven paradigm, thereby giving rise to an interdisciplinary field—Intelligent Aerodynamics (IA). Along two main lines, forward problems in aerodynamic computation and inverse problems in shape design, this paper presents a clear stem for this emerging discipline. It systematically reviews intelligence-empowering approaches across empirical prediction, physical modeling and computation, generative design, and heuristic optimization, while revealing their underlying challenges in interpretability, generalization, and engineering integration. On this basis, the paper further outlines future directions, including the deepening of learning theories, large-scale engineering verification, multi-approach collaboration, and new pathway exploration. All these demonstrate that intelligent aerodynamics is evolving from scattered breakthroughs to systematic integration, and from methodological exploration to becoming credible engineering tools.

Key words: intelligent aerodynamics, aerodynamic computation, aerodynamic shape design, neural operator, deep generative model, reinforcement learning, large language models

CLC Number:

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Intelligence-empowered aerodynamic computation and shape design: Pathways, frontiers and challenges

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