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Techniques for Improving Neural Network-based Aerodynamics Reduced-order Models

Neural networks are applied to create reduced-order models (ROMs) for high-fidelity, nonlinear steady and unsteady CFD aerodynamic simulations by non-intrusively relating outputs (dependent variables) to inputs (independent variables), regardless of the complex physics involved. The present study is conducted with increasing complexity in the aerodynamic system and the corresponding neural network-based ROMs. The primary goal of this paper is to introduce the development and demonstration of new techniques for improving the predictive performance of the reduced-order models. In particular, the benefits of a physics-driven approach for selecting the training inputs and delay states are investigated. The adaptive ROM system is established by performing error estimation through two statistical techniques: cross-validation and bootstrapping.