Antenna Calculation and Autotuning Tool (AntennaCAT)
AntennaCAT is a comprehensive implementation of machine learning to automate, evaluate, and optimize the antenna
design process using EM simulation software. It utilizes a combined antenna designer and internal calculator to
accelerate the CAD construction and EM simulation of several common topologies, while eliminating model disparity
for automated data collection.
See the Release Schedule on the main project README for the immediate feature release plans.
For long-term development, feature documentation, how-to guides, and all other information,
refer to the official AntennaCAT Wiki.
Less formal information, discussion, and planning happens with the #antennacat tag .
This on-going collection of objective functions pulled from popular literature for testing optimizers has 60+ objective functions with
documented solution space, decision space, and objective space with Pareto Front. See README for citations.
State-Machine Based Optimizer Collection
This is an on-going collection of optimizer that integrate with the AntennaCAT project. Some are serious, some are not.
Base Optimizer:pso_python Alternate Version:pso_basic Quantum-Inspired Optimizer:pso_quantum Surrogate Model Version: N/A
Base Optimizer:sweep_python Alternate Version: alternates in base repo
Quantum-Inspired Optimizer: N/A
Surrogate Model Version: N/A
Base Optimizer:bayesian optimization_python Alternate Version: N/A
Quantum-Inspired Optimizer: N/A
Surrogate Model Version: interchangeable surrogate models included in base repo
Base Optimizer:multi_glods_python Alternate Version: N/A
Quantum-Inspired Optimizer: N/A
Surrogate Model Version: N/A
USNC-URSI 2025 NRSM Paper #1237 . Repository for code and demo featured in "Reinforcement Learning Controlled Mechanically Reconfigurable Antennas" presented at the 2025 United States National Committee of URSI National Radio Science Meeting (USNC-URSI NRSM), Boulder, CO, USA.
Repository will be updated pending publication release + with some additional tutorial material as suggested at the conference.
The Repository hosting data and select machine learning examples from the paper "Machine Learning Assisted Hyperparameter Tuning for Optimization"
presented in IEEE AP-S 2024 in Florence, Italy, July 14-19, 2024.
The Repository hosting data and select examples from the paper "Machine Learning Assisted Optimization Methods for Automated Antenna Design"
presented at the 2024 United States National Committee of URSI National Radio Science Meeting (USNC-URSI NRSM), Boulder, CO, USA., 2024.
