{
  "abstract": "PiMiX (Physics-informed Meta-instrument for eXperiments) was introduced for multi-instrument, multi-experiment, and simulation-experiment data fusion in radiographic imaging and tomography (RadIT). Here we present PiMiX 2.02 as an evolving AI-enhanced cyber-physical meta-instrument integrating imaging sensors, near-sensor computing, data fusion, physics-informed inference, and human-supervised AI workflows across X-ray, neutron, and other modalities. Demonstrated capabilities include multimodal CMOS radiation imaging, simulation-assisted sub-pixel neutron localization, and edge-deployed optical-neural-network (ONN) inference; GPU and ONN implementations achieved greater than 96% precision for neutron-event detection with sub-micron localization. A further advance is human-in-the-loop agentic-AI co-analysis of X-ray and neutron images from inertial-confinement-fusion experiments. Beyond conventional preprocessing, the workflow generates competing feature hypotheses, ranks contours using physics-informed evidence, estimates confidence, and presents alternatives for human review. The same architecture adapts to different physics: X-ray analysis emphasizes dark, nonuniform ring structures using deformable closed paths and multi-scale evidence, whereas neutron analysis targets bright emission envelopes using fractional-emission levels, intensity gradients, and cross-filter persistence. We also highlight automated comparison of an as-designed stereolithography model with an X-ray CT reconstruction of an additively manufactured metal lattice. Together, these examples show progression from AI assistance in specific processing tasks to multi-task, multi-domain scientific co-analysis. PiMiX 2.0 further provides a pathway toward PRISM, a RadIT scientific foundation model, and tighter integration of diagnostics, digital representations, inference, and experimental control.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.13347",
  "author_total": 10,
  "authors": [
    {
      "name": "Zhehui Wang"
    },
    {
      "name": "Shanny Lin"
    },
    {
      "name": "Nicholas Amano"
    },
    {
      "name": "Ramya Gurunathan"
    },
    {
      "name": "Katie Liu"
    },
    {
      "name": "Nathan E. Peterson"
    },
    {
      "name": "Michelle A. Espy"
    },
    {
      "name": "Adam Thompson"
    },
    {
      "name": "Amy J. Clarke"
    },
    {
      "name": "Ray T. Chen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/95c3j6tg3km1b.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.13347",
  "fulltext_url": null,
  "id": "95c3j6tg3km1b",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.13347",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "eess.IV"
    ],
    [
      "arxiv",
      "physics.data-an"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2609.13347",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-11",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/95c3j6tg3km1b.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:25Z",
      "native_id": "2609.13347",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.13347"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.data-an",
      "subfield": "computational"
    }
  ],
  "title": "PiMiX 2.02: Toward AI-Driven Data Fusion in Radiographic Imaging and Tomography",
  "title_text": "PiMiX 2.02: Toward AI-Driven Data Fusion in Radiographic Imaging and Tomography",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:25Z",
  "url": "https://research.draconitic.com/paper/95c3j6tg3km1b",
  "venue": "arXiv",
  "versions": []
}