GMCS-UCNCSP
Universal Chaotic-Neuro Computational Substrate Platform
GMCS (Generalized Modular Control System) - Universal Chaotic-Neuro Computational Substrate Platform is a GPU-accelerated research platform combining 1,024 coupled Chua chaotic oscillators with 256×256 3D wave field simulation and energy-based machine learning. The platform bridges chaos theory, computational neuroscience, photonic computing, and real-time audiovisual processing in a unified visual programming environment with 30,000+ lines of production code.
▸FRONTEND:
▸BACKEND:
▸API INTEGRATION:
▸DEPLOYMENT:
_01▸GPU-Accelerated Chaotic Dynamics Engine
Engineered real-time simulation of 1,024 coupled Chua oscillators using JAX JIT compilation and RK4 integration. Achieved 30-120 Hz update rates with multi-GPU support across NVIDIA, AMD, and Apple hardware. Implemented universal modulation matrix for bidirectional parameter routing between all system components.
_02▸3D Wave Field & Photonic Computing Simulation
Built 256×256 3D FDTD PDE solver with complex-valued support for photonic computing research. Implemented 21 signal processing algorithms including optical Kerr effect, four-wave mixing, and nonlinear optics. Real-time visualization with Three.js WebGL rendering at 60 FPS.
_03▸THRML Energy-Based Model Integration
Integrated cutting-edge THRML library (994 lines of custom wrapper code) for heterogeneous energy-based models. Implemented block Gibbs sampling, contrastive divergence learning, higher-order interactions, and conditional sampling. Created bidirectional feedback loop between chaotic oscillators and probabilistic inference.
_04▸Visual Programming Interface & Node Graph System
Developed intuitive drag-and-drop node graph editor with SVG connection engine, persistent localStorage layout, and embedded visualizers. Built 8+ real-time analytics tools including oscilloscope, spectrogram, phase space 3D, energy graphs, and P-bit mappers. Responsive design with react-resizable-panels for desktop/tablet/mobile.
_05▸Real-Time API & WebSocket Streaming Architecture
Architected 80+ REST API endpoints with binary msgpack WebSocket streaming at 100 Hz. Implemented health monitoring (CPU/RAM/GPU), session management, plugin system, and comprehensive testing (100+ test cases, 85%+ coverage). Docker deployment with GPU support and multi-user session handling.
Architected as a full-stack research platform for exploring chaotic-neuro computation and energy-based learning. Built 30,000+ lines of production code (80+ Python files, 50+ TypeScript files) with comprehensive testing and documentation. Integrated multiple ML frameworks (PyTorch, TensorFlow, HuggingFace) for pattern recognition and feature extraction. Implemented novel hybrid system where energy-based models provide feedback to chaotic oscillators, creating bidirectional learning loop. Applications span chaos theory research, photonic computing simulation, computational neuroscience, audio synthesis, and generative art. Open source (MIT license) with active daily development.
GMCS represents a novel approach to programmable chaotic computation, bridging chaos theory, machine learning, and photonic computing in a unified platform. With 30,000+ lines of code, 100+ test cases, multi-GPU support, and real-time visualization, the platform enables researchers and artists to explore emergent behaviors through visual programming. Open source at github.com/gavriel-tech/Chaotic-Neuro-Computational-Substrate. Available for research collaborations, technical partnerships, or integration into educational/commercial platforms.