arXiv AI June 18, 2026 By 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

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

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arXiv:2606. 18402v1 Announce Type: cross Abstract: Traditional microwave filter design typically relies on iterative parameter tuning and predefined topologies, which limits design space and increases development time.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

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Machine Learning Assisted Inverse Design of Pixelated mmWave Patch Antennas

arXiv:2608.23469v1 Announce Type: cross Abstract: A machine learning-assisted framework for the inverse design of pixelated millimetre-wave patch antennas targeting the 22--30 GHz band is presented....

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arXiv Machine Learning

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Interpretable Deep Learning Paradigm for Airborne Transient Electromagnetic Inversion

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arXiv Machine Learning

Jul 23

DREMnet: An Interpretable Denoising Framework for Semi-Airborne Transient Electromagnetic Signal

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llms rag safety

arXiv AI

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Deep Learning-Driven Inverse Design of Doherty Power Amplifiers Using Pixelated Combiners and Dual-State Impedance Synthesis

arXiv:2606. 18395v1 Announce Type: cross Abstract: The output combiner of a Doherty power amplifier (PA) integrates load modulation, impedance matching, and phase compensation within a single network, making its design and synthesis highly challenging.

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arXiv AI

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Deep Learning-Driven Black-Box Doherty Power Amplifier with Pixelated Output Combiner and Extended Efficiency Range

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arXiv Machine Learning

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Deep Learning to Automate Parameter Extraction and Model Fitting of Two-Dimensional Transistors

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