Physics-informed Meta-instrument for eXperiments (PiMiX) with applications to fusion energy

TL;DR

PiMiX is a physics-informed meta-instrument that integrates multi-modal measurements and physics models to enhance data analysis and optimization in nuclear fusion experiments, enabling advanced diagnostics and potential reactor design insights.

Contribution

The paper introduces PiMiX, a novel physics-informed framework that combines multi-instrument data fusion with neural networks for improved fusion experiment analysis.

Findings

Demonstrated super-resolution neutron detection.

Achieved energy-resolved X-ray detection.

Enhanced multi-modal data extraction.

Abstract

Data-driven methods (DDMs), such as deep neural networks, offer a generic approach to integrated data analysis (IDA), integrated diagnostic-to-control (IDC) workflows through data fusion (DF), which includes multi-instrument data fusion (MIDF), multi-experiment data fusion (MXDF), and simulation-experiment data fusion (SXDF). These features make DDMs attractive to nuclear fusion energy and power plant applications, leveraging accelerated workflows through machine learning and artificial intelligence. Here we describe Physics-informed Meta-instrument for eXperiments (PiMiX) that integrates X-ray (including high-energy photons such as $\gamma$-rays from nuclear fusion), neutron and others (such as proton radiography) measurements for nuclear fusion. PiMiX solves multi-domain high-dimensional optimization problems and integrates multi-modal measurements with multiphysics modeling through neural networks. Super-resolution for neutron detection and energy resolved X-ray detection have been demonstrated. Multi-modal measurements through MIDF can extract more information than individual or uni-modal measurements alone. Further optimization schemes through DF are possible towards empirical fusion scaling laws discovery and new fusion reactor designs.