Microwave Engineering and Waveguides

Últimos artículos

• Deep-Learning-Based Pixelated Microwave Filter Design and Characterization using Electro-Optical Electric-Field Measurements

  Han Zhou, Richard Bannister, Caspar Pierce, Haojie Chang, David Widen, Ludvig Fornstedt, Gabriel Melin, Alexander Bohlin, Pontus Lindeberg Fredriksson, Dilbagh Singh, Christian Fager, Koen Buisman · 25 de agosto de 2026

  Traditional microwave filter design typically relies on iterative parameter tuning and predefined topologies, which limits design space and increases development time. This study uses a deep learning approach combining convolutional neural networks with genetic algorithms to automate pixelated micro…

• From Equations to Algorithms and Data: Transforming Microwave Engineering and Education with Machine Learning

  Mehmet Parlak, Islam Guven · 28 de abril de 2026

  Conventional microwave engineering education relies heavily on analytical methods, canonical circuit topologies, and intuition-driven design, which have proven effective at microwave frequencies. However, as systems increasingly operate in the millimeter-wave and terahertz regimes, parasitic effects…

• Efficient RF Passive Components Modeling with Bayesian Online Learning and Uncertainty Aware Sampling

  Huifan Zhang, Pingqiang Zhou · 20 de noviembre de 2025

  Conventional radio frequency (RF) passive components modeling based on machine learning requires extensive electromagnetic (EM) simulations to cover geometric and frequency design spaces, creating computational bottlenecks. In this paper, we introduce an uncertainty-aware Bayesian online learning fr…