Enhancing non-intrusive Reduced Order Models with space-dependent aggregation methods
<|reference_start|>Enhancing non-intrusive Reduced Order Models with space-dependent aggregation methods: In this manuscript, we combine non-intrusive reduced order models (ROMs) with space-dependent aggregation techniques to build a mixed-ROM. The prediction of the mixed formulation is given by a convex linear combination of the predictions of some previously-trained ROMs, where we assign to each model a space-dependent weight. The ROMs taken into account to build the mixed model exploit different reduction techniques, such as Proper Orthogonal Decomposition (POD) and AutoEncoders (AE), and/or different approximation techniques, namely a Radial Basis Function Interpolation (RBF), a Gaussian Process Regression (GPR) or a feed-forward Artificial Neural Network (ANN). The contribution of each model is retained with higher weights in the regions where the model performs best, and, vice versa, with smaller weights where the model has a lower accuracy with respect to the other models. Finally, a regression technique, namely a Random Forest, is exploited to evaluate the weights for unseen conditions. The performance of the aggregated model is evaluated on two different test cases: the 2D flow past a NACA 4412 airfoil, with an angle of attack of 5 degrees, having as parameter the Reynolds number varying between 1e5 and 1e6 and a transonic flow over a NACA 0012 airfoil, considering as parameter the angle of attack. In both cases, the mixed-ROM has provided improved accuracy with respect to each individual ROM technique.<|reference_end|>
@article{ivagnes2024enhancing,
    title={Enhancing non-intrusive Reduced Order Models with space-dependent
  aggregation methods},
    author={Anna Ivagnes, Niccol`o Tonicello, Paola Cinnella, Gianluigi Rozza},
    journal={arXiv preprint arXiv:2403.05710},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.05710},
    primaryClass={math.NA cs.NA}
}