Informer deep learning forecasting for high-speed aeroelastics

This paper examines the suitability and limitations of the Informer deep-learning architecture for forecasting aeroelastic response in high-speed shock-wave/turbulent boundary-layer interaction (SBLI). Dynamic displacement measurements of a thin, compliant panel were obtained in the H2K hypersonic wind tunnel at Mach 5.3 for several cavity-pressure configurations. The Informer, which employs ProbSparse self-attention for long-sequence time-series forecasting, was trained on displacement time histories from a subset of cases and used to predict unobserved scenarios. We systematically varied the forecast horizon (256–768 samples) and data sparsity (1200–4000) and evaluated performance using the normalised root-mean-square error. The model achieved low error (minimum NRMSE 0.19) only for carefully tuned combinations of step size and sparsity, while many other configurations yielded NRMSE values of O(0.5–1) and exhibited systematic under- or overestimation. In particular, the Informer tended to underestimate displacement following recent sharp decays and to overestimate signals that contained an initial near-steady phase. These behaviours reveal a strong sensitivity to hyperparameters and difficulty in capturing the non-stationary, thermally buckled, shock-induced dynamics of the panel. The results indicate that, in its standard form, the Informer is not robust enough to serve as a stand-alone predictor for aeroelastic design. Still, it provides a valuable diagnostic benchmark for long-sequence attention models in hypersonic fluid-structure interaction. The study highlights the need for adaptive or hybrid architectures and suggests future applications in virtual sensing and safety monitoring for high-cost hypersonic testing.