echo 1 on a budget

this repo accompanies a series of blog posts on my personal website https://www.pramit.gg/

Features

• RCS Calculation: Physical optics-based RCS computation for arbitrary geometries
• 3D Optimization: Advanced 3D topology optimization with GPU acceleration
• JAX Integration: GPU-accelerated computations with automatic differentiation
• Topology Optimization: Gradient-based and evolutionary optimization algorithms
• Visualization: Real-time 3D geometry and RCS pattern visualization
• F-117 Inspired: Initial geometries and constraints based on stealth aircraft design principles

Installation

Option 1: Automatic Setup (Recommended)

# Clone the repository
cd nighthawk_rcs

# Run automated setup with JAX GPU support
./scripts/setup.sh

Option 2: Manual Installation

# Create virtual environment (recommended)
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install JAX with GPU support
pip install jax[cuda12] -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html

# Install other dependencies
pip install -r requirements.txt

Verify Installation

# Test overall installation
python tests/test_installation.py

Quick Start

3D Optimization with JAX (Recommended)

from src.rcs_calc_3d import RCS3DCalculator
from src.optimization_3d import TopologyOptimizer3D
from src.geometry_3d import Geometry3D
import trimesh

# Create initial geometry
mesh = trimesh.creation.box(extents=[2, 2, 1])
geometry = Geometry3D(mesh)

# Initialize RCS calculator with GPU acceleration
rcs_calc = RCS3DCalculator(frequency=10e9, use_gpu=True)

# Initialize optimizer
optimizer = TopologyOptimizer3D(rcs_calc)

# Run optimization
optimized_geometry = optimizer.gradient_descent_3d(
    geometry, n_iterations=50, method='adam'
)

# Visualize results
optimizer.visualize_results()

Run Examples

# 3D optimization demo
python examples/rcs_3d_optimization_demo.py

# Interactive Jupyter notebook
jupyter notebook examples/jax_demo.ipynb

# Interactive RCS exploration
jupyter notebook examples/interactive_rcs_exploration.ipynb

Project Structure

nighthawk_rcs/
├── src/                          # Core source code
│   ├── rcs_calc_3d.py           # 3D RCS calculation module
│   ├── geometry_3d.py           # 3D geometry representation
│   ├── optimization_3d.py       # 3D optimization algorithms
│   └── visualization_3d.py      # 3D visualization tools
├── examples/                     # Example scripts and notebooks
│   ├── rcs_3d_optimization_demo.py
│   ├── jax_demo.ipynb
│   └── interactive_rcs_exploration.ipynb
├── tests/                        # Unit tests
│   ├── test_installation.py
│   └── test_optimizations.py
├── docs/                         # Comprehensive documentation
│   ├── PROJECT_SUMMARY.md
│   ├── OPTIMIZATION_SUMMARY.md
│   └── TODO.md
├── scripts/                      # Setup and utility scripts
│   ├── setup.sh
│   └── install_jax.sh
├── visualizations/               # Output visualizations
│   ├── images/                  # PNG/JPG visualization files
│   ├── html/                    # Interactive HTML plots
│   └── models/                  # 3D model files (STL, etc.)
├── config.yaml                   # Configuration file
└── requirements.txt              # Python dependencies

Physics Background

The RCS calculation uses a simplified physical optics (PO) approximation, which is suitable for electrically large objects and provides reasonable accuracy for preliminary design studies.

License

MIT License - See LICENSE file for details