arXiv AI
Machine Learning Assisted Inverse Design of Pixelated mmWave Patch Antennas
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
Hugging Face Trending Papers
Jun 4
Inverse Design of Realizable Metasurface based Absorbers using Improved Conditioning and Diversity Enhanced Progressively Growing GANs
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.
arXiv AI
Jul 2
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.
arXiv Machine Learning
Sep 1
Diffusion-Based Inverse Design of Dielectric Resonator Metasurfaces for Shaping Smart Electromagnetic Environments
The paper presents a conditional diffusion framework for the inverse design of dielectric resonator metasurfaces based on target angular scattering patterns. Trained on T‑matrix simulated geometry‑response pairs, the model learns a distribution of feasible geometries, allowing multiple candidate designs for the ill‑posed inverse problem. The best generated metasurface achieves a mean percentage error of 1.39%, outperforming CMA‑ES optimization and deterministic neural baselines, and can be inferred in about one minute after training.
arXiv Machine Learning
Oct 8
Deep Learning-Based Tri-Hybrid Multi-User MIMO Precoding: The Blessing of EM-Reconfigurable Antennas
The paper introduces Tri-PNet, a deep learning framework that jointly designs electromagnetic (EM), analog, and digital precoding for tri-hybrid multi-user MIMO OFDM systems. By leveraging Conformer architecture, Tri-PNet learns to select radiation patterns and perform hybrid precoding, achieving higher spectral efficiency than random EM precoding and conventional hybrid MIMO while maintaining low online complexity. The study evaluates two radiation-pattern modes—non-regular and 3GPP TR 38.901—showing robust performance even with imperfect channel state information.
arXiv Machine Learning
Oct 28
End-to-End Neural Modeling of EM Response and Design Performance for Free-Form RFIC Passives
The paper presents an end-to-end neural framework that predicts electromagnetic (EM) response and design performance for free-form RFIC passive matching networks across varying frequencies and circuit contexts. It combines a frequency-conditioned neural simulator with a circuit-conditioned neural solver, outputting both explicit EM response and a complementary response to capture orthogonal information. The 21‑million‑parameter model, trained on a large dataset of expert‑designed and random passive layouts, achieves $R^2>0.98$ for most targets and is orders of magnitude faster than traditional EM simulation.
arXiv AI
Jun 9
Physics-Guided Sequence-Based Generative Framework for Acoustic Metamaterial Inverse Design
arXiv:2606. 09266v1 Announce Type: cross Abstract: Acoustic metamaterial (AMM) inverse design is particularly challenging for broadband target responses due to acoustic dispersion: a structure that matches the desired response at one frequency may deviate at others, and modifying geometry to improve one sub-band often perturbs neighboring sub-bands.