Computational modeling of biological dynamic tessellation patterns with comprehensive sensitivity analysis
- Core Code (3 files)
pvt_analysis.py - All 3 methods + comprehensive sensitivity analysis (prior cellular activity knowledge and active learning for materials growth laws discovery) visualization_generator.py - NEW: Tessellation images + animated GIFs quickstart.py - Installation verification
- Methods Implemented
✅ Method 1: Minimal distance with constraint ✅ Method 2: Hexagonal grid + polar random walk (α uniform, ρ sigmoid/ReLU) ✅ Method 3: Particle swarm optimization optimized for biological patterns (w, c1, c2)
- Visualizations Generated 🎬
20 tessellation images (PNG, 10x10 inch, color-coded) 4 animated GIFs showing parameter evolution 8 sensitivity plots (9 subplots each) 4 cell shape distributions 1 method comparison grid Total: 37 visualization files
- Color Coding System
🟢 Green: Hexagonal cells (6-sided) - target! 🟠 Orange: Pentagon/Heptagon (5/7-sided) - good 🔴 Red: Other polygons (3,4,8-sided) - marginal
- Sensitivity Analysis Each parameter tested with 5 gradient levels, 3 trials each:
Method 1: min_distance (0.010 → 0.025) Method 2: rho_scale (0.01 → 0.05) Method 2: n_steps (100 → 1000) Method 3: n_iterations (25 → 150)
- Biological Reference Prominently featured: doi.org/10.1002/advs.202407641
"Curvature-Driven Spatial Patterns in Growing 3D Domains" Yang, Advanced Science, 2024 Compared throughout documentation