Multi-Fidelity Aerodynamic Surrogate Modeling for Conceptual Design Using Graph Neural Networks
Published in Stanford University PhD Thesis, 2026
This dissertation develops multi-fidelity aerodynamic surrogate modeling frameworks for conceptual design using graph neural networks. It introduces methods for augmenting low-fidelity aerodynamic tools with high-fidelity CFD field information, builds and validates graph-based prediction models for delta-wing aerodynamics, and demonstrates their use in design-space exploration and optimization under realistic aerodynamic constraints.
PhD thesis PDF: https://yiren6.github.io/files/Shen_Stanford_University_PhD_Thesis_official.pdf
Recommended citation: Shen, Y. (2026). "Multi-Fidelity Aerodynamic Surrogate Modeling for Conceptual Design Using Graph Neural Networks." Stanford University PhD Thesis.
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