论文标题
机器学习方法将散射参数转换为复杂的介电常数
Machine Learning Approach for Transforming Scattering Parameters to Complex Permittivity
论文作者
论文摘要
这项研究研究了人工神经网络在微波反应化学中常用的颗粒催化剂的复杂介电特性。该研究利用有限元电磁模拟和二维卷积神经网络来求解不同介电的大溶液空间。该卷积神经网络通过监督学习方法和共同的反向传播进行了培训。感兴趣的频率范围在0.1至13.5 GHz之间，介电常数的实际部分范围为1至100，虚部范围为0.0至0.2。使用从同轴航空公司收集的实验数据对网络进行了双重验证。该模型被证明是将实验性或计算衍生的散射参数转换为复杂的介绍性。此外，该模型消除了对迭代解决方案的需求，这些迭代溶液通常在频率依赖散射参数的分段连续性上遇到困难。
This study investigates the application of an artificial neural network to predict the complex dielectric properties of granular catalysts commonly used in microwave reaction chemistry. The study utilizes finite element electromagnetic simulations and two-dimensional convolutional neural networks to solve for a large solution space of varying dielectrics. This convolutional neural network was trained using a supervised learning approach and a common backpropagation. The frequency range of interest was between 0.1 to 13.5 GHz with the real part of the dielectric constants ranging from 1 to 100 and the imaginary part ranging from 0.0 to 0.2. The network was double validated using experimental data collected from a coaxial airline. The model was demonstrated to convert either experimental or computational derived scattering parameter to complex permittivities. Moreover, the model eliminates the need for iterative solutions that often have difficulty with the piecewise continuous nature of frequency dependent scattering parameters.
