Some soft tissues of the human body and biomaterials are considered as multi-layer structures with layer-specific material properties. Identification of the material parameters of these distinct layers is challenging and has attracted considerable attention in recent decades. In this research, an inverse finite element method, which uses the data obtained from simulated suction experiments, is developed. The method employs a limited number of surface-measured data to recover the layer-specific hyperelastic material parameters of a two-layer structure. The Gauss-Newton method is employed to minimize a cost function, which is defined in terms of the difference between measured and calculated displacements of the surface points. The sensitivity analysis during the optimization process is carried out by the finite difference method. The effects of pipette diameter, noise in experimental data, the initial guess and the hyperelastic material model, on the robustness and efficiency of the inverse method are investigated.
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