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Anatomically Inspired Three-dimensional Micro-tissue Engineered Neural Networks for Nervous System Reconstruction, Modulation, and Modeling

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Hybrid U-net-INR network for high precision sparse electromagnetic map reconstruction.

Wanqing Wu¹, Baoguo Yu¹, He Dong¹

¹The 54th Research Institute, China Electronics Technology Group Corporation (CETC), Shijiazhuang 050081, China.

|February 20, 2026

Summary

This summary is machine-generated.

Reconstructing electromagnetic maps from sparse data is challenging. Our hybrid U-Net-Implicit Neural Representation (INR) algorithm achieves high-precision electromagnetic environment mapping, even with limited sampling.

Keywords:

Computer science Earth sciences Engineering

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Area of Science:

Electromagnetic field theory
Signal processing
Artificial intelligence

Background:

Sparse sampling limits electromagnetic environment map reconstruction accuracy.
Traditional interpolation and single-model deep learning methods struggle with nonlinear propagation details and physical consistency.

Purpose of the Study:

To develop a novel algorithm for high-precision electromagnetic environment map reconstruction from sparse data.
To overcome the limitations of existing interpolation and deep learning techniques.

Main Methods:

A hybrid U-Net-Implicit Neural Representation (INR) algorithm was proposed.
The framework integrates U-Net for feature extraction and INR for continuous field mapping.
The method was tested at sparsity levels ranging from 0.005 to 0.05.

Main Results:

The hybrid U-Net-INR algorithm demonstrated superior accuracy compared to Kriging and inverse distance weighting (IDW) interpolation.
Achieved lower Mean Squared Error (MSE) and Mean Absolute Error (MAE), and higher Peak Signal-to-Noise Ratio (PSNR).
Successfully restored nonlinear propagation details and ensured physical consistency.

Conclusions:

The proposed hybrid U-Net-INR algorithm enables high-fidelity electromagnetic map reconstruction from extremely sparse data.
Provides a robust solution for accurate electromagnetic mapping under practical sampling constraints.
Enhances spectrum management and situation awareness capabilities.