An intelligent model selection method based on graph representation learning

To select the most credible simulation model among multiple alternatives with dynamic and correlated outputs, an intelligent model selection method based on graph representation learning (GRL) is proposed, which treats the model selection problem as a reference data categorization problem. To model the correlations between the output variables, the evaluation data are initially transformed from their traditional two-dimensional format into a graph-structured format based on the distance correlation coefficients. Then, graph isomorphism networks (GINs) are employed to achieve GRL and evaluation data classification. The graph embeddings produced by GRL, which represent the interdependencies and dynamic evolutionary patterns among variables, enable the categorization of reference data as originating from one of the alternative models. Finally, the most credible simulation model is determined based on the category probabilities of the multisample reference data. The effectiveness of the proposed method in feature extraction and model selection is demonstrated through an application example on aerodynamic parameter models of a flight vehicle.