{
  "abstract": "Electromagnetic inverse scattering is a nonlinear and ill-posed computational imaging problem, where accurate reconstruction is challenging due to measurement limitations, noise, and high computational costs, especially for 3-D imaging. Although physics-driven neural networks (PDNNs) reduce the dependence on labeled training data, existing accelerated PDNN frameworks often rely on preliminary reconstruction-based region selection, which may introduce instability when the selected region is inaccurate. In this paper, a coordinate-residual physics-driven neural network (CRPDNN) is proposed for 3-D electromagnetic inverse scattering. CRPDNN represents the unknown complex contrast distribution using normalized spatial coordinates and a residual convolutional network, whose parameters are optimized by enforcing consistency between the measured and model-predicted scattered fields. Unlike existing subregion-accelerated PDNN approaches, CRPDNN does not require a preliminary reconstruction, thereby avoiding dependence on its accuracy. For the reported noise-free 3-D synthetic cases, CRPDNN achieves an average relative error of 2.10\\%, compared with 7.97\\% for CSI and 3.99\\% for $L_{2/3}$-FBE-WCIE, while providing approximately 5.5- and 12.1-fold speedups over the two baselines, respectively. Additional 2-D comparisons further demonstrate its stability and computational efficiency relative to existing PDNN frameworks. CRPDNN also maintains reliable reconstruction performance under noisy measurements, and the 3-D Fresnel experiments further indicate its potential for practical imaging applications.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2608.09382",
  "author_total": 5,
  "authors": [
    {
      "name": "Yutong Du"
    },
    {
      "name": "Zicheng Liu"
    },
    {
      "name": "Bo Qi"
    },
    {
      "name": "Yali Zong"
    },
    {
      "name": "Peixian Han"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2tgz684x4mm41.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2608.09382",
  "fulltext_url": null,
  "id": "2tgz684x4mm41",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2608.09382",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "physics.app-ph"
    ],
    [
      "arxiv",
      "physics.comp-ph"
    ],
    [
      "openalex",
      "2204|Microwave Imaging and Scattering Analysis"
    ]
  ],
  "openalex": "W7202195366",
  "pdf_url": "https://arxiv.org/pdf/2608.09382",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/2tgz684x4mm41.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:00Z",
      "native_id": "2608.09382",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2608.09382"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.app-ph",
      "subfield": "applied-instrumentation"
    },
    {
      "field": "physics",
      "origin": "arxiv:physics.comp-ph",
      "subfield": "computational"
    }
  ],
  "title": "Coordinate-Residual Physics-Driven Neural Network for Inverse Scattering Imaging",
  "title_text": "Coordinate-Residual Physics-Driven Neural Network for Inverse Scattering Imaging",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:00Z",
  "url": "https://research.draconitic.com/paper/2tgz684x4mm41",
  "venue": "arXiv",
  "versions": []
}