EE ZOOM Seminar: Conditional Inverse Sampling for the Design of Antennas in Complex Environments

25 ביוני 2025, 15:00 
סמינר זום 
EE ZOOM Seminar: Conditional Inverse Sampling for the Design of Antennas in Complex Environments

https://Intuitive.zoom.us/j/96079672856?pwd=voqmtJqqGpz3BXC1UlYBnX05P475gu.1
 

Electrical Engineering Systems Seminar

 

Speaker: Moshe Yelisevitch

M.Sc. student under the supervision of Prof. Haim Yelisevitch

 

Wednesday, 25th June 2025, at 15:00

 

Conditional Inverse Sampling for the Design of Antennas in Complex Environments

Abstract

The design of compact, high-performance antennas remains a formidable challenge due to the intricate relationship between structural geometry, material, and electromagnetic behavior. Traditional design approaches rely on iterative tuning and brute-force search, often requiring extensive electromagnetic (EM) simulations that are computationally expensive and time-consuming. Furthermore, real-world constraints such as environmental interactions, fabrication limitations, and nonlinear geometry-performance dependencies make it difficult to generate antennas that are both optimal and physically realizable. To address these challenges, we propose a novel Conditional Neural Inverse Transform Sampler (C-NITS) framework for inverse antenna design. Unlike conventional optimization-based approaches, our method learns to map desired electromagnetic characteristics, including reflection coefficient and radiation pattern, to a distribution of feasible antenna geometries, directly generating solutions that satisfy both performance and manufacturability constraints. Our approach extends the Neural Inverse Transform Sampler (NITS) to a conditional formulation, enabling controllable sampling based on environmental parameters such as substrate properties and nearby obstructions. By leveraging a learned inverse model and a fast surrogate simulation network, our framework efficiently explores high-dimensional design spaces without exhaustive full-wave EM simulations. A key feature of our method is its ability to generate diverse, multiple antenna solutions, rather than converging to a single optimal design. This enables engineers to explore multiple feasible configurations that satisfy design objectives while considering fabrication and environmental constraints. Furthermore, we incorporate a structured selection mechanism that filters generated designs to ensure real-world feasibility criteria. Our experiments demonstrate that C-NITS significantly outperforms traditional optimization techniques and existing deep-learning-based inverse design models in terms of EM similarity - both in numerical accuracy (e.g., pixel-wise comparisons) and structural similarity - as well as in engineering-relevant performance metrics. By combining conditional generative modeling with physics-aware constraints, our framework advances the state of automated antenna design, making it more adaptable and scalable for real-world applications.

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