Magnetic field estimation using Gaussian process regression for interactive wireless power system design

2025-10-22

AI Summary

Traditional electromagnetic simulations (e.g., method of moments) for wireless power transfer (WPT) systems incur prohibitive computational costs, hindering real-time interactive design. To address this, this paper proposes a fast full-field and power transfer efficiency modeling method based on Gaussian process regression (GPR). It is the first to apply GPR to full near-field magnetic prediction in coupled resonant systems, integrating 3D adaptive meshing with active learning to accurately capture the nonlinear relationship between geometric parameters and electromagnetic responses. The method maintains high efficiency and robustness even with complex structures—such as ferrimagnetic shielding—and achieves sub-second magnetic field prediction with a mean absolute error below 6%. This significantly enhances both the real-time performance and accuracy of interactive WPT system design.

Technology Category

• Machine Learning: Hardware-aware ML
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

Wireless power transfer (WPT) with coupled resonators offers a promising solution for the seamless powering of electronic devices. Interactive design approaches that visualize the magnetic field and power transfer efficiency based on system geometry adjustments can facilitate the understanding and exploration of the behavior of these systems for dynamic applications. However, typical electromagnetic field simulation methods, such as the Method of Moments (MoM), require significant computational resources, limiting the rate at which computation can be performed for acceptable interactivity. Furthermore, the system's sensitivity to positional and geometrical changes necessitates a large number of simulations, and structures such as ferromagnetic shields further complicate these simulations. Here, we introduce a machine learning approach using Gaussian Process Regression (GPR), demonstrating for the first time the rapid estimation of the entire magnetic field and power transfer efficiency for near-field coupled systems. To achieve quick and accurate estimation, we develop 3D adaptive grid systems and an active learning strategy to effectively capture the nonlinear interactions between complex system geometries and magnetic fields. By training a regression model, our approach achieves magnetic field computation with sub-second latency and with an average error of less than 6% when validated against independent electromagnetic simulation results.

Problem

• Accelerating magnetic field simulation for interactive wireless power design
• Reducing computational cost of electromagnetic simulations using machine learning
• Enabling real-time efficiency estimation for near-field coupled resonator systems

Innovation

• Gaussian Process Regression for magnetic field estimation
• 3D adaptive grid systems for nonlinear interaction capture
• Active learning strategy enabling sub-second computation latency