Physics-Guided Intelligent Tomography Sensing System for Non-Destructive Testing Based on Neuromorphic Eddy Current Circuit Array and Physical Electromagnetic Dynamics Model

Current non-destructive tomography sensing systems, particularly in the domain of eddy current sensing, lack the capability to dynamically and intelligently optimize sensing parameters in response to time-varying environments. To address this limitation, we propose a hybrid tomography sensing system that integrates physical artificial intelligence (PAI) with an electromagnetic dynamics model for intelligent sensing. This system combines a physical recurrent neural network (PRNN) entities performed by the programmable planar coil arrays with its digital counterpart in a closed-loop configuration, facilitating forward inference and the backpropagation of errors respectively. Additionally, the system leverages physics-guided methods based on electromagnetic field dynamics and employs a combination of standard neural network training techniques to optimize the parameters of PRNN, enabling real-time adaptive optimization of sensing. Theoretical modelling of the PRNN has been rigorously conducted in this study. Furthermore, high-fidelity electromagnetic tomography (EMT) results for non-destructive testing are demonstrated, showcasing the potential of physics-guided analogue AI in EMT sensing. The results shows that the electromagnetic controlling system, optimized through physical AI approach, achieves higher precision results with lower complexity compared to standard digital implementations in eddy current testing. This also provides a novel potential way for optimizing PNNs, thereby enhancing the acceleration of physical AI and projecting diversity of physical information into analogue AI solver.