Hydrodynamic performance estimation of undulating elastic plates using the Fourier neural operator

Fluid–structure interactions (FSI) underpin diverse biological and engineering systems, yet computational modeling of these interactions remains resource-intensive and constrained by high-dimensional parameter spaces. To address these limitations, we develop a data-driven, two-stage surrogate modeling framework leveraging Fourier neural operators (FNO) to accurately predict the hydrodynamic performance of biomimetic elastic propulsors across a broad range of design parameters. Our approach first maps geometric and physical parameters to the propulsor dynamic bending pattern and then uses this bending pattern to predict thrust, power, and efficiency. Trained using direct FSI simulation data, our surrogate model achieves three orders of magnitude speedup compared to the direct simulations while maintaining high accuracy. Crucially, the use of intermediate kinematic modeling significantly reduces the overall model size and enhances accuracy. Our FNO modeling framework enables rapid exploration of large design spaces, indicating strong potential for application in the design and optimization of FSI problems.