Inverse-Design-of-Patch-Antennas

This repository hosts a simple demonstration of a deep learning approach for the inverse design of patch antennas. The goal is to explore energy-efficient designs and to significantly reduce simulation cost compared to conventional methods.

The following papers have been published in relation to this repository:

Inverse Design of Reflective Metasurface Antennas Using Deep Learning from Small-Scale Statistically Random Pico-Cells
Energy Efficient Design of Low-Profile Wideband Microstrip Patch Antennas Using Deep Learning

Overview

This repository presents a novel inverse design methodology for patch antennas using deep learning techniques. The project encompasses several key components:

Data Generation: Generation of a comprehensive dataset for training the deep learning model.
Data Preprocessing: Preprocessing of the dataset to ensure high-quality input for the network.
Network Training & Testing: Implementation and training of the deep learning model, followed by rigorous testing to validate its performance.
Prediction: Utilization of the trained model to predict optimal patch antenna designs.
Inverse Design: Process of designing patch antennas based on the predictions from the deep learning model.

Getting Started

To get started with this simple demonstration of inverse design for patch antennas, follow these steps:

Clone the Repository Clone this repository to your local machine using the following command:
```
git clone https://github.com/youxch/Inverse-Design-of-Patch-Antennas.git
```
Install Dependencies Install all necessary dependencies by running:
```
pip install -r requirements.txt
```
Data Preparation The training dataset is prepared through automated simulations controlled by code, resulting in the train_data.txt file which is properly formatted with the structural parameters and simulation results for patch antennas.
Model Training Run the train.py script to preprocess the training data and train the Multilayer Perceptron (MLP) network:
```
python train.py
```
The trained model weights will be saved as saved-model-5000.h5, with ‘5000’ indicating the number of iterations or a training metric.
Model Prediction and Inverse Design Use the predict.py script to make predictions and perform inverse design.
```
python predict.py
```
Ensure that the trained model weights file saved-model-5000.h5 is available before running predictions.

Contact

For any questions or suggestions, please open an issue or directly contact the maintainers.

License

This project is licensed under the MIT License - see the LICENSE.md file for details.

Cite

If you use the code provided in this repository, please cite the following papers:

You, X. C., & Lin, F. H. (2024). Inverse Design of Reflective Metasurface Antennas Using Deep Learning from Small-Scale Statistically Random Pico-Cells. Microwave and Optical Technology Letters, 2024, 66(2), e34068.
You, X. C., & Lin, F. H. (2023). Energy Efficient Design of Low-Profile Wideband Microstrip Patch Antennas Using Deep Learning. In 2023 International Conference on Microwave and Millimeter Wave Technology (ICMMT), Qingdao, China, 2023, pp. 1-3.

Thank you for your cooperation in acknowledging the original work.

dxs527 / Inverse-design-of-patch-antennas