Use AI to Explore Design Space, Analyze, and Optimize Antennas
This example collection showcases how AI‑enhanced antenna modeling significantly improves design productivity by reducing reliance on repeated full‑wave simulations Antenna Toolbox™. AI‑based antenna models dramatically speed up common design tasks still closely matching EM simulation results. AI-based modeling also enables you to explore a broad design space (evaluating hundreds to thousands geometries), which is not feasible when relying solely on full‑wave simulations. Using the examples in this collection, you can:
Compute half-power beamwidth (HPBW) and peak radiation in seconds with AI and validate the values against full-wave EM simulation.
Explore frequency response and generate surface-response maps that reveal parameter sensitivities.
Run full-factorial parametric sweeps to rapidly identify design candidates that meet multi-metric constraints.
Optimize antenna geometry with a TR‑SADEA global optimizer integrated with AI-driven evaluation.
These examples together provide a unified process for performance analysis, design space exploration, and optimization, with outputs that are comparable to full EM simulations.
These examples use a horn antenna operating at 10 GHz for illustration.
Examples Description
Beamwidth and Peak Radiation Analysis: This example uses an AI‑based model of the horn antenna to compute its HPBW and peak gain. It compares the model’s predictions with full‑wave EM simulation results to evaluate accuracy and computational cost. The example shows how AI modeling delivers fast and reliable estimates of radiation characteristics and reduces analysis time during iterative design workflows.
Frequency Response and Surface Plot Analysis: This example evaluates the frequency‑dependent behavior of the horn antenna using its AI‑based model. It performs a wideband sweep (±30%) to analyze how beamwidth and peak gain vary with frequency. The example also generates surface‑response plots by varying tunable geometric parameters (±15%) to visualize sensitivities in resonant frequency, bandwidth, beamwidth, and peak gain. These analyses show how specific design parameters influence antenna performance across the design space.
Full Factorial Parametric Sweep: This example performs a full‑factorial exploration of five tunable horn antenna parameters over a ±15% range using an AI‑based evaluation workflow. It quickly computes resonant frequency, bandwidth, peak gain, and beamwidth for each configuration. It identifies candidate geometries that satisfy multiple performance constraints by filtering the results against design criteria. The example uses AI models to do an efficient design‑space search before formal optimization.
Optimize and Analyze Antenna: This example uses the TR‑SADEA optimizer combined with AI‑based performance evaluation to optimize the horn‑antenna geometry. It aims to maximize peak gain and bandwidth while keeping the resonant frequency near the 10 GHz design point and maintaining a beamwidth below 30°. Full‑wave EM analysis validates the optimized design for accuracy. The example shows how AI models accelerate optimization by replacing computationally expensive EM solvers.
This hybrid AI–EM workflow improves both the efficiency and reliability of horn‑antenna design and extends naturally to other antenna classes and frequency bands.
