用机器学习方法将散射参数转换为复介电常数
Machine Learning Approach for Transforming Scattering Parameters to Complex Permittivity
摘要：
本研究探讨应用人工神经网路来预测微波反应化学中常用的粒状触媒之复杂介电性质。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频率的大解空间。该卷积神经网络使用监督学习方法和常见的反向传播进行训练。感兴趣的频率范围为0.1至13.5 GHz之间，介电常数的实部范围为1至100，虚部范围为0.0至0.2。该网络的双重验证，使用从同轴航空公司收集的实验数据。该模型被证明可以将实验或计算导出的散射参数转换为复折射率。此外，该模型消除了对迭代解的需要，迭代解通常难以满足频率相关散射参数的分段连续性质。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
