PiMiX 2.02: Toward AI-Driven Data Fusion in Radiographic Imaging and Tomography

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.