https://github.com/saro0307/generative-ai-for-space-based-architecture

Generative AI for Space-Based Architecture is a project that combines artificial intelligence and physics simulations to design sustainable habitats for extraterrestrial environments. Using GANs (Generative Adversarial Networks), the project generates habitat blueprints optimized for planetary conditions like temperature, gravity, and radiation.
https://github.com/saro0307/generative-ai-for-space-based-architecture

architecture artificial-intelligence deep-learning gan generativeai machine-learning physics physics-simulation planetary-science space spaceexploration sustainability

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Generative AI for Space-Based Architecture is a project that combines artificial intelligence and physics simulations to design sustainable habitats for extraterrestrial environments. Using GANs (Generative Adversarial Networks), the project generates habitat blueprints optimized for planetary conditions like temperature, gravity, and radiation.

• Host: GitHub
• URL: https://github.com/saro0307/generative-ai-for-space-based-architecture
• Owner: saro0307
• License: mit
• Created: 2024-11-11T14:03:57.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2024-11-11T14:12:45.000Z (3 months ago)
• Last Synced: 2024-11-11T15:19:38.242Z (3 months ago)
• Topics: architecture, artificial-intelligence, deep-learning, gan, generativeai, machine-learning, physics, physics-simulation, planetary-science, space, spaceexploration, sustainability
• Language: Jupyter Notebook
• Homepage:
• Size: 25.4 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Generative AI for Space-Based Architecture

## Project Overview

This project leverages generative AI and physics-based simulations to design sustainable habitats for extraterrestrial environments. Using Generative Adversarial Networks (GANs) conditioned on planetary data, the project aims to generate habitat designs optimized for environmental factors such as temperature, gravity, and radiation. 

This tool could be used to explore potential habitat designs for different planets, simulate environmental viability, and visualize structural blueprints.

## Features

- **Data Preprocessing**: Normalization and preparation of planetary data for model input.

- **Generative Habitat Design**: A GAN model that generates habitat features based on environmental constraints.

- **Environmental Suitability Check**: Physics-based checks for temperature, gravity, and radiation.

- **Blueprint Visualization**: Visual representation of generated habitat designs.

- **Web Interface**: Flask-based web app for user interaction, enabling easy input of planetary parameters.

## Project Structure

```markdown

space-habitat-ai/

│

├── data/                       # Dataset and pre-trained models

├── models/                     # GAN and model training scripts

├── simulation/                 # Simulation and physics-based checks

├── utils/                      # Helper functions for data processing and visualization

├── web_app/                    # Flask web app for user interaction

│   ├── templates/              # HTML templates for Flask

│   ├── static/                 # CSS and JavaScript files

│   └── app.py                  # Main Flask app

├── tests/                      # Unit tests for code components

├── notebooks/                  # Jupyter notebooks for exploration

├── requirements.txt            # Python dependencies

├── README.md                   # Project documentation

└── config.yaml                 # Configuration file

```

## Getting Started

### Prerequisites

- Python 3.8+

- pip for package management

- Virtual environment (optional but recommended)

### Installation

1. Clone this repository:

    ```bash

    git clone https://github.com/saro0307/space-habitat-ai.git

    cd space-habitat-ai

    ```

2. Install required packages:

    ```bash

    pip install -r requirements.txt

    ```

### Usage

1. **Prepare Data**: Add planetary and environmental data to `data/planetary_data.csv`.

2. **Train the GAN Model**:

    ```bash

    python models/gan_training.py

    ```

   Adjust parameters in `gan_training.py` if needed.

3. **Run Simulations**: Verify environmental suitability with `simulation/environmental_check.py`.

4. **Launch the Web App**:

    ```bash

    python web_app/app.py

    ```

   Access the app in your browser at `http://127.0.0.1:5000`.

5. **Blueprint Visualization**: Use `utils/visualization.py` to visualize habitat blueprints.

## Example

To generate a habitat design for Mars with specific environmental parameters:

1. Run the web app and enter Mars parameters.

2. The AI model will generate a design based on input parameters.

3. Results and suggestions based on environmental suitability will be displayed.

## Contributing

Contributions are welcome! Please follow these steps:

1. Fork the project.

2. Create your feature branch (`git checkout -b feature/YourFeature`).

3. Commit your changes (`git commit -m 'Add feature'`).

4. Push to the branch (`git push origin feature/YourFeature`).

5. Open a pull request.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Acknowledgements

- The project uses foundational research in GANs and physics-based simulations.

- Python libraries like `PyTorch`, `Flask`, `pandas`, and `matplotlib` power the backend.

---

**Note**: This project is experimental and intended for research and educational purposes only.

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