arXiv preprint
Neural-field design of broadband Rayleigh-wave carpet cloaks under microstructure realisability constraints
A neural-field and differentiable-FEM framework for designing broadband Rayleigh-wave carpet cloaks within realizable Cauchy materials and mapping the optimized fields to explicit manufacturable microstructures.
Abstract / summary
The paper formulates two-dimensional Rayleigh-wave carpet-cloak design as a PDE-constrained optimization problem within physically realizable Cauchy elasticity. A coordinate-based neural field and differentiable finite element solver optimize symmetric stiffness and density fields for single-frequency and broadband performance. The optimized fields are then mapped to explicit microstructures using homogenization, conditional diffusion, neural-field inverse design, and nearest-neighbor selection.
Methodology
- PDE-constrained optimization of a two-dimensional Rayleigh-wave carpet cloak
- Coordinate-based neural field coupled to a differentiable finite element solver
- Single-frequency and broadband optimization of symmetric Cauchy stiffness and density fields
- Microstructure-constrained realization using homogenization, conditional diffusion, neural-field inverse design, and nearest-neighbor selection
- Validation with homogenized and fully resolved finite element simulations
Datasets and benchmarks
- Database of homogenized microstructures
Principal results
- The optimized Cauchy design approaches the ideal transformation-based cloak in finite element simulations.
- After projection to explicit microstructures, the homogenized model recovers approximately 97% of the defect-free reference surface-displacement magnitude.
- Direct simulation of the fully resolved microstructured geometry recovers approximately 76% at the design frequency.
Technologies and methods
- Neural fields
- Differentiable finite element methods
- PDE-constrained optimization
- Conditional diffusion
- Homogenization
- Finite element simulation
Evidence boundary
Benchmark claims and numerical values are paper-reported results for the named protocol. Potential applications are not evidence of completed deployments.