Fourier Neural Operators for Structural Dynamics Models: Challenges, Limitations and Advantages of Using a Spectrogram Loss (2511.08753v1)

Abstract: Fourier Neural Operators (FNOs) have emerged as promising surrogates for partial differential equation solvers. In this work, we extensively tested FNOs on a variety of systems with non-linear and non-stationary properties, using a wide range of forcing functions to isolate failure mechanisms. FNOs stand out in modeling linear systems, regardless of complexity, while achieving near-perfect energy preservation and accurate spectral representation for linear dynamics. However, they fail on non-linear systems, where the failure manifests as artificial energy dissipation and manipulated frequency content. This limitation persists regardless of training dataset size, and we discuss the root cause through discretization error analysis. Comparison with LSTM as the baseline shows FNOs are superior for both linear and non-linear systems, independent of the training dataset size. We develop a spectrogram-based loss function that combines time-domain Mean Squared Error (MSE) with frequency-domain magnitude and phase errors, addressing the low-frequency bias of FNOs. This frequency-aware training eliminates artificial dissipation in linear systems and enhances the energy ratios of non-linear systems. The IEA 15MW turbine model validates our findings. Despite hundreds of degrees of freedom, FNO predictions remain accurate because the turbine behaves in a predominantly linear regime. Our findings establish that system non-linearity, rather than dimensionality or complexity, determines the success of FNO. These results provide clear guidelines for practitioners and challenge assumptions about FNOs' universality.