Dynamic Resonance Rooting (DRR) Framework

A computational framework for analyzing Complex Adaptive Systems, based on the research paper "Dynamic Resonance Rooting: A Computational Framework for Complex Adaptive Systems" by Christopher Woodyard.

Dynamic Resonance Rooting (DRR) – Official Implementation

Dynamic Resonance Rooting (DRR) is a computational framework designed to analyze complex adaptive systems (CAS) through the identification of dominant oscillatory patterns (resonances) and the underlying causal structures that drive system behavior.

Overview

The DRR framework aims to provide:

• Real-time signal detection from time-series data,
• Rooting analysis to identify causal and hierarchical dependencies within the system,
• Resonance Depth (RD) as an early-warning metric for system instability or critical transitions.

Features:

• Signal Detection: Using spectral analysis and phase-space reconstruction.
• Causal Modeling: Maps the underlying causes of system behavior via transfer entropy.
• Resonance Depth: A stability metric to detect critical transitions.
• Visualizations: Real-time graphs, resonance depth evolution, and symbolic field representations.

Key Benefits:

• Real-time System Monitoring: Adaptable to various domains including finance, healthcare, and defense.
• Dynamic Forecasting: Predictive analysis of system stability and risk.
• Scalable: Can be implemented in both small-scale research projects and large, real-time operational systems.

Use Cases:

• Military & Defense: Monitoring the resilience of complex defense systems and forecasting system failures.
• Healthcare: Predicting and mitigating critical health events like cardiac arrhythmias or neurological disorders.
• Finance: Detecting market crashes or systemic risk in economic systems.

Installation:

# Clone the repository
git clone topherchris420/dynamic-resonance-rooting.git
cd dynamic-resonance-rooting

# Install required dependencies
pip install -r requirements.txt

Generative Design Suite for Acoustic Metamaterials (Separate Workflow)

This repository now includes a separate system-level workflow focused on converting one audio recording into a fabrication-ready 3D structure:

• Module: drr_framework.generative_design_suite.GenerativeDesignSuite
• Goal: Bridge raw waveform data to manifold mesh geometry suitable for STL/OBJ export.

Core Engine Architecture

1. Audio Dissection (DSP)

   • Ingest waveform, denoise/normalize.
   • Perform short-time FFT and extract magnitude, phase, and band-energy trajectories.
   • Produce spectral tensors for geometry synthesis.

2. 3D Resonance Field Mapping

   • Map frequency → X, time → Y, amplitude → Z.
   • Inject phase coherence as local curvature and orientation cues.
   • Convert point-cloud density to manifold mesh (marching cubes + repair).

3. Generative Metamaterial Exploration

   • Use spectral peaks as procedural growth seeds.
   • Modulate lattice cell size and anisotropy from local spectral energy.
   • Blend macro shell behavior with internal diffusion lattice topology.

4. Digital Twin + Export

   • Run structural checks (stress/deflection), modal targets, and acoustic response.
   • Close the loop with geometry optimization until constraints pass.
   • Export watertight geometry as STL or OBJ.

Recommended Python Stack

• DSP: librosa, numpy, scipy.signal, soundfile
• Topology / Meshing: scikit-image, open3d, pyvista
• Generative Geometry: trimesh, compas, blender bpy API, pygalmesh
• Digital Twin: fenics, sfepy, pyelmer, openmdao
• Fabrication Export: trimesh, meshio, numpy-stl

Closed-Loop Logic Flow

audio_in -> FFT dissection -> spectral-to-resonance field map -> manifold mesh extraction -> metamaterial growth -> digital twin simulation -> optimization feedback -> STL/OBJ export -> physical print -> measurement-based recalibration

Example

from drr_framework import GenerativeDesignSuite

suite = GenerativeDesignSuite()
architecture = suite.architecture()
print(architecture["closed_loop_flow"])