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Noah Parsons edited this page Aug 26, 2025 · 3 revisions

Symbolic-Topological Framework for Physical Systems

Welcome to the comprehensive wiki documentation for the Symbolic-Topological Framework for Physical Systems. This framework provides a unified, formal approach to describing and reasoning about physical systems using symbolic-topological language, category theory, and neural-symbolic AI.

Navigation

  1. Theoretical Foundations

    • Core mathematical concepts
    • Foundational principles
    • Mathematical framework overview

  2. Category Theory Basics

    • Basic definitions and concepts
    • Functors and natural transformations
    • Applications in physical systems

  3. Symbolic-Topological Representations

    • Topology fundamentals
    • Symbolic representation schemes
    • Integration with physical systems

  4. Neural-Symbolic Integration

    • Neural networks architecture
    • Symbolic reasoning integration
    • Hybrid system design

  5. Implementation Guide

    • Setup and installation
    • Basic usage
    • Advanced configurations

  6. API Reference

    • Complete API documentation
    • Function references
    • Class hierarchies

  7. Examples and Use Cases

    • Practical applications
    • Tutorial notebooks
    • Case studies

  8. Contributing Guidelines

    • How to contribute
    • Code standards
    • Development workflow

  9. FAQ

    • Common questions
    • Troubleshooting
    • Best practices

Quick Start

To get started with the framework:

  1. Clone the repository:
git clone https://github.com/GuiloScion/Symbolic-Topological-Framework-Physical-Systems.git

Install dependencies:
bash
 pip install -r requirements.txt


Check out the [Implementation Guide](https://github.com/copilot/c/Implementation-Guide) for detailed setup instructions.


Overview
This framework combines symbolic reasoning, topological analysis, and neural networks to create a powerful system for modeling and analyzing physical systems. Key features include:

- Unified mathematical framework based on category theory
-Symbolic-topological representation of physical systems
-Integration with neural networks for learning and inference
-Extensible architecture for custom implementations
-Comprehensive toolset for analysis and visualization
-Support and Community

Submit issues on our [GitHub Issues page](https://github.com/GuiloScion/Symbolic-Topological-Framework-Physical-Systems/issues)

Join discussions in our [Discussions forum](https://github.com/GuiloScion/Symbolic-Topological-Framework-Physical-Systems/discussions)

Check our [Security Policy](https://github.com/GuiloScion/Symbolic-Topological-Framework-Physical-Systems/security/policy)


License
This project is licensed under the MIT License - see the [LICENSE](https://github.com/GuiloScion/Symbolic-Topological-Framework-Physical-Systems/blob/main/LICENSE) file for details.
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