Multi-Fidelity Aerodynamic Surrogate Modeling for Conceptual Design
Date:
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 has strong focus on introducing 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.