用机器学习加速智能表面设计，减少海量仿真耗时。
Physics-Informed Machine Learning for Efficient Reconfigurable Intelligent Surface Design
- 融合MLP与双端口网络建模反射系数，替代复杂电磁仿真
- 实验与仿真结果一致，验证了设计方法有效性
- 适合无线通信与雷达领域工程师快速优化RIS
可重构智能表面（RIS）是一种集成大量反射单元的二维周期结构，可通过数字方式调控电磁波，在无线通信与雷达探测中具有巨大潜力。然而，传统RIS设计严重依赖耗时的全波电磁（EM）仿真。为此，本文提出一种机器学习辅助的高效RIS设计方法：通过多层感知机神经网络（MLP）与双端口网络相结合，构建准确且快速的RIS单元反射系数预测模型，显著减少网络训练中的电磁仿真工作量。基于该方法实际设计并制备了一款RIS，实验结果与仿真高度吻合，验证了所提方法在RIS设计中的有效性。
原文摘要 · Abstract (English)
Reconfigurable intelligent surface (RIS) is a two-dimensional periodic structure integrated with a large number of reflective elements, which can manipulate electromagnetic waves in a digital way, offering great potentials for wireless communication and radar detection applications. However, conventional RIS designs highly rely on extensive full-wave EM simulations that are extremely time-consuming. To address this challenge, we propose a machine-learning-assisted approach for efficient RIS design. An accurate and fast model to predict the reflection coefficient of RIS element is developed by combining a multi-layer perceptron neural network (MLP) and a dual-port network, which can significantly reduce tedious EM simulations in the network training. A RIS has been practically designed based on the proposed method. To verify the proposed method, the RIS has also been fabricated and measured. The experimental results are in good agreement with the simulation results, which validates the efficacy of the proposed method in RIS design.
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