Neural-field design of broadband Rayleigh-wave carpet cloaks under microstructure realisability constraints

Abstract

Transformation elasticity provides appropriate material distributions for elastodynamic cloaks but the required stiffness tensors generally violate the minor symmetries of Cauchy elasticity and are difficult to realise using conventional materials. Existing approaches restore these symmetries by modifying the transformed tensor, producing only an approximate cloak. In this work rather than modifying the transformed tensor we seek the best performing cloak within the class of Cauchy materials. We formulate 2D Rayleigh wave carpet cloak design as an optimisation problem governed by partial differential equations. Using a coordinate based neural-field and a differentiable finite element model solver we optimise symmetric stiffness and density fields by minimising wave field distortion. Both single frequency and broadband optimisation are considered, with the broadband model trained over multiple frequencies. Physical realisability is addressed using a database of homogenised microstructures through conditional diffusion, neural-field inverse design, and nearest-neighbour selection. FEM simulations show that the optimised Cauchy design approaches the ideal transformation-based cloak. After projection onto explicit microstructures, the homogenised representation recovers approximately 97% of the defect free reference surface-displacement magnitude, while direct FEM simulation of the fully resolved microstructured geometry recovers approximately 76%