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Data-Driven Approach for the Floquet Propagator Inverse Problem Solution

Floquet theory is a classical tool for the analysis of periodic structures’ acoustics. However, it may be challenging to analyze material properties for complex cases, whereas the composite material’s integral characteristics are obtained relatively straightforward. The paper shows the data-driven approach to inverse displacement data to the theoretical propagation constant approximation and consequently the material parameters using machine learning methods, namely a datadriven symbolic regression. Such an approach may be used to determine the material acoustic parameters in complex cases. Another application is a wave finite element analysis speedup. Two waveguide models are considered: axial rod and circular membrane.