Solving forward and inverse wave scattering via boundary integral equations and deep learning. Applications to cloaking design

TL;DR

This paper introduces a deep learning framework using boundary integral equations to design and evaluate cloaking devices for wave scattering, demonstrating improved performance with geometry-specific configurations.

Contribution

It presents a unified, data-driven approach combining boundary element methods and neural networks for systematic cloaking design and comparison.

Findings

Object-fitted cloaks outperform simple circular designs in scattering reduction

The approach is effective for various geometries including circular, star-shaped, and kite-shaped objects

The method can be extended to more complex geometries and broadband applications

Abstract

We propose a deep learning framework based on an encoder-decoder architecture for the design and evaluation of cloaking devices, demonstrated in this work for two-dimensional wave propagation governed by the Helmholtz equation. The cloaks under consideration are concentric layered media surrounding the object, whose geometry and material parameters determine the scattering response. We consider circular and object-fitted layer configurations and parameterize all designs by the layer thicknesses, enabling a unified representation for direct comparison of different cloaks for the same object. Training data are generated using a boundary element formulation suitable for geometries where analytic solutions are not available, and neural networks are trained with standard hyperparameters on geometry-specific datasets. The proposed approach is applied to circular, star-shaped, and kite-shaped objects. Results show that object-fitted configurations consistently outperform simpler circular-layer designs in scattering reduction, highlighting the importance of geometry in cloaking performance. Overall, we present a flexible, data-driven approach for systematic comparison of cloaking strategies, with potential extension to more complex geometries and broadband settings.