TNFR Engines Hub

Centralized location for all TNFR mathematical and optimization engines.

🏗️ Engine Categories

1. Self-Optimization (`self_optimization/`)

TNFRSelfOptimizingEngine: Automatic network optimization
Physics: Based on ∂EPI/∂t = νf · ΔNFR(t) nodal equation
Capabilities: Node targeting, multi-operator optimization, real-time metrics

2. Pattern Discovery (`pattern_discovery/`)

TNFREmergentPatternEngine: Mathematical pattern detection
UnifiedPatternDetector: Operator sequence analysis
Discoveries: Eigenmodes, spectral cascades, fractal scaling, symmetry breaking

3. Computation (`computation/`)

GPUEngine: GPU-accelerated TNFR computations
FFTEngine: Fast Fourier Transform processing
Performance: High-throughput parallel processing

4. Integration (`integration/`)

EmergentIntegrationEngine: Multi-scale analysis
Capabilities: Hierarchical coupling, cross-scale information flow

🚀 Quick Usage

Direct Engine Access

python

from tnfr.engines.self_optimization import TNFRSelfOptimizingEngine
from tnfr.engines.pattern_discovery import TNFREmergentPatternEngine

# Self-optimization
engine = TNFRSelfOptimizingEngine(network)
success, metrics = engine.step(node_id)

# Pattern discovery
pattern_engine = TNFREmergentPatternEngine()
patterns = pattern_engine.discover_patterns(network)

Via SDK (Recommended)

python

from tnfr.sdk import TNFR

# Integrated auto-optimization
net = TNFR.create(50).random(0.3).auto_optimize()

# Fluent API chains
result = TNFR.create(100).evolve(10).auto_optimize().results()

📚 Documentation

Theory: AGENTS.md - Single Source of Truth
Grammar: UNIFIED_GRAMMAR_RULES.md
Examples: examples/ - Sequential tutorials
Individual engines: See README.md in each subdirectory

🎯 Engine Selection Guide

Use Case	Engine	Import
Auto-optimize network	Self-Optimization	`from tnfr.engines.self_optimization import TNFRSelfOptimizingEngine`
Discover math patterns	Pattern Discovery	`from tnfr.engines.pattern_discovery import TNFREmergentPatternEngine`
GPU acceleration	Computation	`from tnfr.engines.computation import GPUEngine`
Multi-scale analysis	Integration	`from tnfr.engines.integration import EmergentIntegrationEngine`
Simple workflows	SDK	`from tnfr.sdk import TNFR`

🧮 TNFR Physics Foundation

All engines implement TNFR principles:

Nodal equation: ∂EPI/∂t = νf · ΔNFR(t)
Structural-field tetrad: Φ_s, |∇φ|, K_φ, ξ_C (only π is a genuine structural scale)
Unified grammar: U1-U6 operator constraints
Canonical invariants: 6 fundamental preservation laws
Structural field tetrad: Complete multi-scale characterization

Status: Production ready, fully integrated with TNFR v0.0.1

TNFR Engines Hub

Centralized location for all TNFR mathematical and optimization engines.

🏗️ Engine Categories

1. Self-Optimization (`self_optimization/`)

TNFRSelfOptimizingEngine: Automatic network optimization
Physics: Based on ∂EPI/∂t = νf · ΔNFR(t) nodal equation
Capabilities: Node targeting, multi-operator optimization, real-time metrics

2. Pattern Discovery (`pattern_discovery/`)

TNFREmergentPatternEngine: Mathematical pattern detection
UnifiedPatternDetector: Operator sequence analysis
Discoveries: Eigenmodes, spectral cascades, fractal scaling, symmetry breaking

3. Computation (`computation/`)

GPUEngine: GPU-accelerated TNFR computations
FFTEngine: Fast Fourier Transform processing
Performance: High-throughput parallel processing

4. Integration (`integration/`)

EmergentIntegrationEngine: Multi-scale analysis
Capabilities: Hierarchical coupling, cross-scale information flow

🚀 Quick Usage

Direct Engine Access

python

from tnfr.engines.self_optimization import TNFRSelfOptimizingEngine
from tnfr.engines.pattern_discovery import TNFREmergentPatternEngine

# Self-optimization
engine = TNFRSelfOptimizingEngine(network)
success, metrics = engine.step(node_id)

# Pattern discovery
pattern_engine = TNFREmergentPatternEngine()
patterns = pattern_engine.discover_patterns(network)

Via SDK (Recommended)

python

from tnfr.sdk import TNFR

# Integrated auto-optimization
net = TNFR.create(50).random(0.3).auto_optimize()

# Fluent API chains
result = TNFR.create(100).evolve(10).auto_optimize().results()

📚 Documentation

Theory: AGENTS.md - Single Source of Truth
Grammar: UNIFIED_GRAMMAR_RULES.md
Examples: examples/ - Sequential tutorials
Individual engines: See README.md in each subdirectory

🎯 Engine Selection Guide

Use Case	Engine	Import
Auto-optimize network	Self-Optimization	`from tnfr.engines.self_optimization import TNFRSelfOptimizingEngine`
Discover math patterns	Pattern Discovery	`from tnfr.engines.pattern_discovery import TNFREmergentPatternEngine`
GPU acceleration	Computation	`from tnfr.engines.computation import GPUEngine`
Multi-scale analysis	Integration	`from tnfr.engines.integration import EmergentIntegrationEngine`
Simple workflows	SDK	`from tnfr.sdk import TNFR`

🧮 TNFR Physics Foundation

All engines implement TNFR principles:

Nodal equation: ∂EPI/∂t = νf · ΔNFR(t)
Structural-field tetrad: Φ_s, |∇φ|, K_φ, ξ_C (only π is a genuine structural scale)
Unified grammar: U1-U6 operator constraints
Canonical invariants: 6 fundamental preservation laws
Structural field tetrad: Complete multi-scale characterization

Status: Production ready, fully integrated with TNFR v0.0.1

README.md

TNFR Engines Hub

🏗️ Engine Categories

1. Self-Optimization (self_optimization/)

2. Pattern Discovery (pattern_discovery/)

3. Computation (computation/)

4. Integration (integration/)

🚀 Quick Usage

Direct Engine Access

Via SDK (Recommended)

📚 Documentation

🎯 Engine Selection Guide

🧮 TNFR Physics Foundation

README.md

TNFR Engines Hub

🏗️ Engine Categories

1. Self-Optimization (self_optimization/)

2. Pattern Discovery (pattern_discovery/)

3. Computation (computation/)

4. Integration (integration/)

🚀 Quick Usage

Direct Engine Access

Via SDK (Recommended)

📚 Documentation

🎯 Engine Selection Guide

🧮 TNFR Physics Foundation

1. Self-Optimization (`self_optimization/`)

2. Pattern Discovery (`pattern_discovery/`)

3. Computation (`computation/`)

4. Integration (`integration/`)

1. Self-Optimization (`self_optimization/`)

2. Pattern Discovery (`pattern_discovery/`)

3. Computation (`computation/`)

4. Integration (`integration/`)