Welcome to 🤖 GS-Agent-Hub

Contents:

• 🤖 GS-Agent: Generative Simulation Intelligence Hub
  • Table of Content
  • 🤔 1 | Preamble
  • 🌍 2 | Purpose
  • 🎯 3 | Vision
  • 🧱 4 | Bricks (Simulation Kernels)
  • 🧠 5 | Problem Archive
  • 🔁 6 | Mutual Intelligence Workflow
  • 🧭 7 | Contribution Guidelines
  • 🗺️ 8 | Roadmap
  • 🙌 9 | Why This Matters
  • ✅ 🔭 10 | What’s Next
• 🚀 Contrastive Deep Learning in Generative Simulation (GS)
  • 🧠 Abstract
  • 1️⃣ GS-Agents and Simulation Reasoning
  • 2️⃣ Motivation: Scaling Laws in Sparse Data
  • 3️⃣ Contrastive GS: Principles and Interpretations
  • 4️⃣ Dimensionality Reduction and Scaling Structure
  • 5️⃣ Methodological Synthesis
  • 6️⃣ Visual Architecture
  • 7️⃣ Future Directions
  • 🧩 Conclusion
• 🔬 Contrastive Scaling Laws in Diffusivity Using Neural Networks
  • ✨ Abstract
  • 1. 🧠 Theoretical Background
  • 2. 🧪 Synthetic Data Generation
  • 3. 🔁 Contrastive Dataset Construction
  • 4. 🤖 Neural Network Training
  • 5. 🔢 Symbolic Regression Baseline
  • 6. 📊 Visual Diagnostics & Performance Metrics
  • 7. 📉 Dimensionality Reduction and Scaling Structure
  • 8. 🧩 Hybrid Methodology & Scaling Laws
  • ✅ Conclusion
  • ⚙️ Appendix: Execution Environment
• 📘 Kernel Documentation: sfppy.evaluate
  • 🔧 Function
  • 📥 Inputs
  • 📤 Outputs
  • 🧠 Assumptions
  • ⚠️ Limitations
  • 🔗 See Also
• 📘 Kernel Documentation: radigen.solve
  • 🔧 Function
  • 📥 Inputs
  • 📤 Outputs
  • 🧠 Assumptions
  • ⚠️ Limitations
  • 🔗 See Also
• 📘 Kernel Documentation: sig2dna.encode
  • 🔧 Function
  • 📥 Inputs
  • 📤 Outputs
  • 🧠 Assumptions
  • ⚠️ Limitations
  • 🔗 See Also
• API Reference