发表机构
Los Alamos National Laboratory; The University of Texas at Austin; University of Michigan; NVIDIA; Brown University（洛斯阿拉莫斯国家实验室; 德克萨斯大学奥斯汀分校; 密歇根大学; 英伟达; 布朗大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
PiMiX 2.02作为AI增强的元仪器，集成多模态成像与物理信息推理，实现高精度中子定位及人在回路的智能体协同分析，推动科学数据融合与基础模型发展。
AI 中文摘要
PiMiX（物理信息元仪器）是为放射成像与断层扫描（RadIT）中的多仪器、多实验及仿真-实验数据融合而引入的。在此，我们介绍PiMiX 2.02，它是一个不断发展的、AI增强的网络物理元仪器，集成了成像传感器、近传感器计算、数据融合、物理信息推理以及跨X射线、中子和其他模态的人工监督AI工作流。已展示的能力包括多模态CMOS辐射成像、仿真辅助亚像素中子定位，以及边缘部署的光学神经网络（ONN）推理；GPU和ONN实现在中子事件检测中达到了超过96%的精度，并具有亚微米定位能力。进一步的进展是人在回路的智能体AI协同分析来自惯性约束聚变实验的X射线和中子图像。除了传统的预处理，该工作流生成竞争性特征假设，使用物理信息证据对轮廓进行排序，估计置信度，并呈现备选方案供人工审查。同一架构适应不同的物理场景：X射线分析强调使用可变形闭合路径和多尺度证据的暗色非均匀环状结构，而中子分析则针对使用分数发射水平、强度梯度和跨滤波器持久性的亮发射包络。我们还重点介绍了按设计的光刻模型与增材制造金属晶格的X射线CT重建的自动比较。总之，这些示例展示了从特定处理任务中的AI辅助到多任务、多领域科学协同分析的进展。PiMiX 2.0进一步为PRISM（一个RadIT科学基础模型）以及诊断、数字表示、推理和实验控制的更紧密集成提供了途径。
英文摘要
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.
Comments12 pages, 5 figures
