Hi Dev Community! 👋
I'm excited to share SO-HMS (Self-Optimizing Hyper-Manifold System), a framework designed to handle complex, multi-objective optimization across diverse topological spaces.
This project aims to bridge the gap between deep learning, physical simulation, and economic equilibrium modeling by utilizing a 4-phase synchronization mechanics approach.
- GitHub Repository: https://github.com/ryujinchoi/so-hmns
🛠️ Core Capabilities
- Continuous Spectral Smoothness: Manages structural manifold resilience using Laplace-Beltrami operators.
- Exponential Boltzmann Attenuation: Ensures stable learning velocity and avoids division-by-zero singularities.
- Topological Information Invariance: Uses KL-Divergence to ensure entropy conservation.
- Autonomous GradNorm Engine: Dynamically balances multi-objective gradients.
🚀 Get Involved
The repository includes a main.py pipeline to demonstrate the system's ability to balance loss functions autonomously. I would highly appreciate your thoughts and engineering feedback.
- Repository: https://github.com/ryujinchoi/so-hmns
