Black Hole — applying computational intelligence to a domain-specific scientific or engineering challenge.
Topics: general-relativity · black-hole-physics · computational-astrophysics · deep-learning · neural-networks · physics-simulation · schwarzschild-metric · accretion-disk · event-horizon-simulation · ray-tracing-geodesics
Black Hole is a domain-specific computational project that combines machine learning, data analysis, or scientific simulation with domain expertise to address a real problem in science or engineering. The project demonstrates that effective AI/ML is not just about algorithms — it requires deep understanding of the domain, the data it generates, and the domain-specific evaluation criteria that determine whether a model is actually useful.
The pipeline covers data acquisition or generation, preprocessing and feature engineering appropriate to the domain, model training and evaluation using domain-standard metrics, and interpretation of results in domain-meaningful terms. All code is structured for reproducibility: random seeds are fixed, data splits are deterministic, and results are logged with all hyperparameters.
Visualisations are designed for the domain audience: not generic accuracy curves, but domain-specific plots that communicate the model's utility in the language of the field.
Domain-specific AI applications have higher impact than generic benchmark performance. A model that solves a real scientific measurement problem or engineering decision task creates value that transcends its accuracy score. This project was built to demonstrate that combination of domain knowledge and ML can produce practically useful results.
Domain Data Input
│
Domain-specific preprocessing
│
ML / Computational Model
│
Domain-specific evaluation
│
Interpretable output + visualisation
Data loading, cleaning, and preprocessing tailored to the specific format and conventions of the domain dataset.
Domain-informed feature construction that encodes relevant physical, biological, or engineering prior knowledge.
Trained predictive or classification model with domain-appropriate evaluation metrics.
Result visualisations that communicate findings in the language of the domain, not just generic ML plots.
Fixed seeds, deterministic data splits, and logged hyperparameters for reproducible results.
Command-line batch mode for processing multiple domain data samples.
Results exportable in domain-standard formats for use in further analysis tools.
Inline code documentation explaining the domain context for each processing step.
| Library / Tool | Role | Why This Choice |
|---|---|---|
| Python | Primary language | Scientific Python ecosystem |
| NumPy / SciPy | Numerical computing | Array operations, scientific functions |
| pandas | Data management | Tabular data handling |
| Matplotlib / Plotly | Visualisation | Domain-specific plots |
| scikit-learn / PyTorch | ML model | Classification or regression |
- Python 3.9+ (or Node.js 18+ for TypeScript/JS projects)
pipornpmpackage manager- Relevant API keys (see Configuration section)
git clone https://github.com/Devanik21/Black-hole.git
cd Black-hole
pip install -r requirements.txt
python main.pypython main.py --input data.csv --output results/
# Or launch interactive interface
streamlit run app.py| Variable | Default | Description |
|---|---|---|
INPUT_PATH |
data/ |
Input data directory |
OUTPUT_PATH |
results/ |
Output directory for results |
MODEL_PATH |
model.pkl |
Trained model path |
Copy
.env.exampleto.envand populate all required values before running.
Black-hole/
├── README.md
└── ...
- Integration with domain-specific data APIs for live data ingestion
- Advanced model architectures (GNN, Transformer) for complex domain data
- Uncertainty quantification for domain-critical predictions
- Collaborative annotation interface for domain expert feedback
- Publication-ready figure generation for research reports
Contributions, issues, and feature requests are welcome. Please:
- Fork the repository
- Create a feature branch (
git checkout -b feature/your-feature) - Commit your changes (
git commit -m 'feat: add your feature') - Push to your branch (
git push origin feature/your-feature) - Open a Pull Request
Please follow conventional commit messages and ensure any new code is documented.
Domain expertise is required to correctly interpret and use these results. Please consult relevant literature and domain experts before applying outputs to real-world decisions.
Devanik Debnath
B.Tech, Electronics & Communication Engineering
National Institute of Technology Agartala
This project is open source and available under the MIT License.
Crafted with curiosity, precision, and a belief that good software is worth building well.