arXiv AI
Machine Learning Assisted Inverse Design of Pixelated mmWave Patch Antennas
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Metasurfaces enable precise manipulation of electromagnetic waves for applications such as beam steering, sensing, and stealth technology. However, inverse design of metasurfaces with targeted EM responses remains challenging due to the computational expense of iterative full wave simulation driven optimization and the limited conditioning fidelity and diversity of existing generative approaches.
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Meta-Transfer Learning for mmWave Beam Alignment
arXiv:2607. 00860v1 Announce Type: cross Abstract: Millimeter-wave (mmWave) beam alignment plays a critical role in next-generation wireless systems, yet its efficient implementation remains challenging.
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Diffusion-Based Inverse Design of Dielectric Resonator Metasurfaces for Shaping Smart Electromagnetic Environments
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Physics-Guided Sequence-Based Generative Framework for Acoustic Metamaterial Inverse Design
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TARGet: Topology-Aware Fusion-based Radio Frequency Circuit Functional Modeling using Graph Neural Networks
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Inductively Scalable, Single-Step Neural Surrogates for Wave-Scattering Inverse Problems
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