Neural Operators as Fast Surrogate Models for the Transmission Loss of Parameterized Sonic Crystals

Neural operators serve as efficient, data-driven surrogate models for complex physical and engineering problems. In this work, we demonstrate that neural operators can directly learn the key properties of sonic crystals, a type of acoustic metamaterial consisting of a lattice of parameterized shapes. We predict the transmission loss curve, a critical characteristic in applications, bypassing the expensive meshing and solving steps typical of classical techniques. We evaluate established architectures, DeepONet (DON) and Fourier Neural Operator (FNO), alongside two new ones, Deep Neural Operator (DNO) and Deep Cat Operator (DCO), which demonstrate significant performance improvements. In our experiments, all models achieve high accuracy, while being up to 10⁶ times faster than the traditional method, significantly advancing practical real-time metamaterial design.