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

Jakob Elias Wagner, Samuel Burbulla, Miguel de Benito Delgado, Johannes D. Schmid

Keywords: Neural Operators, Sonic Crystals, Acoustic Metamaterials, Surrogate Models, Transmission Loss Prediction, Data-Driven Modeling, Real-Time

TL;DR: Neural operators accurately predict sonic crystal properties up to 5 million times faster than traditional methods, streamlining real-time metamaterial design.

Abstract: 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.

Submission Number: 24