A Deep Learning-Enhanced Fourier Method for the Multi-Frequency Inverse Source Problem with Sparse Far-Field Data

TL;DR

This paper presents a hybrid deep learning and Fourier-based approach to accurately reconstruct sources in inverse Helmholtz problems from sparse, noisy far-field data, outperforming traditional methods.

Contribution

It introduces a novel framework combining Fourier methods with deep neural networks and a transfer learning strategy for robust, high-fidelity source reconstruction.

Findings

Achieves accurate reconstructions with up to 100% noise.

Outperforms traditional spectral methods under sparse data.

Generalizes well to unseen source geometries.

Abstract

This paper introduces a hybrid computational framework for the multi-frequency inverse source problem governed by the Helmholtz equation. By integrating a classical Fourier method with a deep convolutional neural network, we address the challenges inherent in sparse and noisy far-field data. The Fourier method provides a physics-informed, low-frequency approximation of the source, which serves as the input to a U-Net. The network is trained to map this coarse approximation to a high-fidelity source reconstruction, effectively suppressing truncation artifacts and recovering fine-scale geometric details. To enhance computational efficiency and robustness, we propose a high-to-low noise transfer learning strategy: a model pre-trained on high-noise regimes captures global topological features, offering a robust initialization for fine-tuning on lower-noise data. Numerical experiments demonstrate that the framework achieves accurate reconstructions with noise levels up to 100%, significantly outperforms traditional spectral methods under sparse measurement constraints, and generalizes well to unseen source geometries.