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πŸ’“ ENTRO-PULSE (E-LAB-09)

Periodic Entropy Pulsing and Informational Wave Management in High-Velocity AI Systems

PyPI version DOI License: MIT Python 3.11+ Tests Coverage E-LAB


Overview

ENTRO-PULSE introduces Periodic Entropy Pulsing (PEP) β€” a control paradigm that transforms entropy flow management from continuous suppression into a rhythmically-managed oscillatory regime.

Rather than fighting entropy accumulation reactively, PEP orchestrates it: drawing on the cardiac pulsing model, PWM principles from power electronics, and the Kuramoto model of coupled oscillator synchronization to turn system stress into a structured, predictable wave.

Core Contributions

Component Full Name Role
EPWM Entropy Pulse Width Modulation Adaptive duty-cycle control of entropy flow
RRL Rhythmic Resonance Law Anti-phase Kuramoto synchronization across agents
PGC Pulse-Ghost Controller Integration bridge with ENTRO-GHOST (E-LAB-08)

Validated Results

Metric ENTRO-PULSE Baseline
Throughput gain +38.7% β€”
Collapse events under burst overload 0% 23.4%
Peak network load reduction 86.1% β€”
Burst survival rate 100% β€”

Installation

pip install entro-pulse

Quick Start

EPWM Controller

from entro_pulse import EntropyPulseController

# Initialize with angular frequency and max duty cycle
epwm = EntropyPulseController(omega=0.8, delta_max=0.7)

# Execute a control step
result = epwm.step(psi=0.85, u_base=0.1)

print(f"Duty cycle : {result.duty_cycle:.3f}")
print(f"Output     : {result.u_output:.3f}")

Pulse-Ghost Controller

Integrates entropic memory traces from ENTRO-GHOST (E-LAB-08).

from entro_pulse import PulseGhostController

pgc = PulseGhostController(omega=0.8, delta_max=0.7, zeta=0.65, rho=0.4)
result = pgc.step(psi=0.85, u_base=0.1)

print(f"Ghost trace : {result.ghost_trace:.3f}")
print(f"Ghost pull  : {result.ghost_pull:.3f}")

Rhythmic Resonance Law β€” Distributed Systems

from entro_pulse import RhythmicResonanceController

rrl = RhythmicResonanceController(n_agents=8, K=0.5)
phases = rrl.step(100)

r = rrl.order_parameter()   # r β†’ 0 confirms anti-phase synchronization
print(f"Order parameter: {r:.4f}")

Documentation

Resource Link
Website https://entro-pulse.netlify.app
Research Paper https://doi.org/10.5281/zenodo.19547863
API Reference https://entro-pulse.readthedocs.io

Project Structure

ENTRO-PULSE/
β”‚
β”œβ”€β”€ entro_pulse/
β”‚   β”œβ”€β”€ __init__.py
β”‚   β”œβ”€β”€ epwm.py           # EPWM Controller  β€” Eq 3.1, 3.2, 4.1, 4.2
β”‚   β”œβ”€β”€ rrl.py            # RRL Controller   β€” Eq 5.1, 5.2, 5.3
β”‚   β”œβ”€β”€ pgc.py            # Pulse-Ghost Controller β€” Eq 6.1, 6.2, 6.3
β”‚   └── utils.py          # Simulation utilities
β”‚
β”œβ”€β”€ tests/
β”‚   β”œβ”€β”€ test_epwm.py      # 8 tests
β”‚   β”œβ”€β”€ test_rrl.py       # 5 tests
β”‚   β”œβ”€β”€ test_pgc.py       # 8 tests
β”‚   └── test_utils.py     # 4 tests
β”‚
β”œβ”€β”€ examples/
β”‚   β”œβ”€β”€ example_epwm.py
β”‚   β”œβ”€β”€ example_rrl.py
β”‚   └── example_pgc.py
β”‚
β”œβ”€β”€ results/
β”‚   β”œβ”€β”€ daily_report_2026-04-14.txt
β”‚   β”œβ”€β”€ weekly_report_week15_2026.txt
β”‚   β”œβ”€β”€ monthly_report_april_2026.txt
β”‚   β”œβ”€β”€ alerts.log
β”‚   └── coverage_report_2026-04-14.txt
β”‚
β”œβ”€β”€ docs/
β”‚   β”œβ”€β”€ conf.py
β”‚   β”œβ”€β”€ index.rst
β”‚   └── api.rst
β”‚
β”œβ”€β”€ Netlify/
β”‚   β”œβ”€β”€ index.html
β”‚   β”œβ”€β”€ dashboard.html
β”‚   β”œβ”€β”€ reports.html
β”‚   └── documentation.html
β”‚
β”œβ”€β”€ bin/
β”‚   └── run_simulation.py
β”‚
β”œβ”€β”€ scripts/
β”œβ”€β”€ data/
β”œβ”€β”€ dist/
β”‚   └── entro-pulse-1.0.0.tar.gz
β”‚
β”œβ”€β”€ pyproject.toml
β”œβ”€β”€ requirements.txt
β”œβ”€β”€ requirements-dev.txt
β”œβ”€β”€ Dockerfile
β”œβ”€β”€ Makefile
β”œβ”€β”€ VERSION
β”œβ”€β”€ CITATION.cff
β”œβ”€β”€ AUTHORS.md
β”œβ”€β”€ CHANGELOG.md
β”œβ”€β”€ CONTRIBUTING.md
β”œβ”€β”€ SECURITY.md
β”œβ”€β”€ DEPLOY.md
β”œβ”€β”€ INSTALL.md
└── COMPLETION.md

Codebase Statistics

Metric Value
Python modules 5
Test files 4
Test cases 25 / 25 passed
Coverage 89%
Governing equations 12+

EntropyLab Research Program

ENTRO-PULSE is the ninth project in the EntropyLab series β€” a unified research program bridging thermodynamic entropy, Shannon information theory, and AI systems control.

E-LAB Project Focus
01 ENTROPIA Theoretical foundations
02 ENTRO-AI AI inference stability
03 ENTRO-CORE Core entropy measurement
04 ENTRO-ENGINE System coupling
05 ENTRO-EVO Adaptive weighting
06 ENTRO-NET Distributed synchronization
07 ENTRO-QUANTUM Probabilistic states
08 ENTRO-GHOST Entropic memory
09 ENTRO-PULSE Periodic pulsing
10 ENTRO-MANIFESTO Unified manifesto

β†’ Program home: entropia-lab.netlify.app


Citation

@software{baladi2026entropulse,
  author    = {Samir Baladi},
  title     = {ENTRO-PULSE: Periodic Entropy Pulsing and Informational Wave Management
               in High-Velocity AI Systems},
  year      = {2026},
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.19547863},
  note      = {EntropyLab E-LAB-09},
  url       = {https://doi.org/10.5281/zenodo.19547863}
}

License

MIT License Β© 2026 Samir Baladi Ronin Institute / Rite of Renaissance Β· ORCID 0009-0003-8903-0029


"A system that pulses does not merely survive its entropy dynamics β€” it dances with them."

β€” EntropyLab Research Program

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