This repository contains a MATLAB application for predicting the bandgap ratio and performing inverse design of phononic crystals using a trained neural network. The tool leverages advanced machine learning techniques to optimize and tailor phononic crystal structures for specific bandgap properties. Key features include:
- Bandgap Ratio Prediction: Accurately predicts the bandgap ratio of phononic crystals using a trained neural network model.
- Inverse Design: Facilitates the design of phononic crystals with desired bandgap properties through an inverse design approach.
- User-Friendly Interface: MATLAB-based graphical interface for ease of use and accessibility.
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Prediction Tab:
- Adjust values of e/a, r/a, and h/a using sliders.
- Instantly view the predicted bandgap ratio in a text box.
- Automatically update the 3D view of the phononic crystal unit cell within the app.
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Inverse Design Tab:
- Set boundary ranges for e/a, d/a, and h/a using range sliders.
- Input the desired bandgap ratio.
- Click "Compute" to perform inverse design with minimal optimization using a trained neural network and particle swarm optimization.
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Clone the Repository:
git clone https://github.com/Amirhossein4096/PhononicBGRPredictDesign.git
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Install Dependencies:
- Ensure you have MATLAB installed.
- Additional required toolboxes: Neural Network Toolbox, etc.
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Run the Application:
- Open MATLAB.
- Navigate to the cloned repository directory.
- Run the main script:
app_main.m
.
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Prediction Tab:
- Use the sliders to set the values of e/a, r/a, and h/a.
- View the predicted bandgap ratio and 3D unit cell visualization.
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Inverse Design Tab:
- Set boundary ranges for e/a, d/a, and h/a.
- Input the desired bandgap ratio.
- Click "Compute" to start the inverse design process.
This project is licensed under the MIT License - see the LICENSE file for details.
For any questions or suggestions, please contact [email protected].
A MATLAB app for predicting bandgap ratios and inverse design of phononic crystals using a trained neural network.
Phononic Bandgap Predictor and Inverse Design App using Neural Networks