Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.09333 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915930863304704 |
|---|---|
| author | Du, Yutong Liu, Zicheng Wu, Bo Kou, Jingwei Li, Hang Li, Changyou Zong, Yali Qi, Bo |
| author_facet | Du, Yutong Liu, Zicheng Wu, Bo Kou, Jingwei Li, Hang Li, Changyou Zong, Yali Qi, Bo |
| contents | This paper presents an improved physics-driven neural network (IPDNN) framework for solving electromagnetic inverse scattering problems (ISPs). A new Gaussian-localized oscillation-suppressing window (GLOW) activation function is introduced to stabilize convergence and enable a lightweight yet accurate network architecture. A dynamic scatter subregion identification strategy is further developed to adaptively refine the computational domain, preventing missed detections and reducing computational cost. Moreover, transfer learning is incorporated to extend the solver's applicability to practical scenarios, integrating the physical interpretability of iterative algorithms with the real-time inference capability of neural networks. Numerical simulations and experimental results demonstrate that the proposed solver achieves superior reconstruction accuracy, robustness, and efficiency compared with existing state-of-the-art methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_09333 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Improved Physics-Driven Neural Network to Solve Inverse Scattering Problems Du, Yutong Liu, Zicheng Wu, Bo Kou, Jingwei Li, Hang Li, Changyou Zong, Yali Qi, Bo Machine Learning This paper presents an improved physics-driven neural network (IPDNN) framework for solving electromagnetic inverse scattering problems (ISPs). A new Gaussian-localized oscillation-suppressing window (GLOW) activation function is introduced to stabilize convergence and enable a lightweight yet accurate network architecture. A dynamic scatter subregion identification strategy is further developed to adaptively refine the computational domain, preventing missed detections and reducing computational cost. Moreover, transfer learning is incorporated to extend the solver's applicability to practical scenarios, integrating the physical interpretability of iterative algorithms with the real-time inference capability of neural networks. Numerical simulations and experimental results demonstrate that the proposed solver achieves superior reconstruction accuracy, robustness, and efficiency compared with existing state-of-the-art methods. |
| title | Improved Physics-Driven Neural Network to Solve Inverse Scattering Problems |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2512.09333 |