Speaker: 郭玉坤教授，哈尔滨工业大学

Inviter: 刘晓东

Title: A Deep-Learning-Enhanced Fourier Method for the Inverse Source Problem with Sparse Measurements

Language: Chinese

Time & Venue: 2026.09.15 10:00-11:00 思源楼S807

Abstract: In this talk, we introduce a hybrid computational framework for the multi-frequency inverse source problem governed by the Helmholtz equation. By integrating the 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 classical method with a deep convolutional neural network, which serves as the 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 moderate noise levels.