Multi-Fidelity Aerodynamic Surrogate Modeling for Conceptual Design Using Graph Neural Networks

Speaker: Yiren Shen, Stanford University
July 9, 2026

Presentation

Abstract

Conceptual aircraft design requires major engineering decisions under limited information. In practice, low-fidelity aerodynamic tools are routinely used because higher-fidelity simulations are often unavailable or too expensive for early-stage design. This creates a fidelity-cost gap: the level of analysis fidelity needed for confident decision-making is not available at the stage with the greatest downstream impact. This challenge is especially important in supersonic transport design, where nonlinear aerodynamic effects make considerations such as low-speed stability difficult to assess using conventional conceptual design tools.

This talk presents MF-VortexNet, a multi-fidelity aerodynamic surrogate framework that uses graph neural networks to learn field-level corrections to low-fidelity pressure predictions from high-fidelity CFD data. By operating on pressure-field information, MF-VortexNet improves interpretability, enables the evaluation of arbitrary aerodynamic quantities, and supports generalization across a prescribed design space. The talk will also discuss an open-source multi-fidelity aerodynamic dataset, empirical scaling behavior of surrogate error with training data size, and integration of the framework into a conceptual design environment for stability-constrained shape optimization. Together, these results show that multi-fidelity machine learning offers a promising path toward improving confidence in early-stage aerodynamic design.

Biography

Yiren Shen is currently a postdoctoral researcher in the Department of Aeronautics and Astronautics at Stanford University. He received his Ph.D. in 2026 from Stanford University, where he was advised by Professor Juan J. Alonso. His research focuses on multi-fidelity methods, surrogate modeling, physics AI, and conceptual aircraft design. He is particularly interested in developing data-driven methods that combine multi-fidelity data sources to improve early-stage design, uncertainty quantification, and optimization for complex aerospace systems.

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