ai4plasma

Getting Started

  • 1. Installation Guide
  • 2. Quick Start
  • 3. Basic Concepts
  • 4. Configuration

User Guide

  • 1. Physics-Informed Machine Learning (PIML)
  • 2. Neural Operators
  • 3. Training, Logging, Checkpoints
  • 4. Utilities
  • 5. Plasma Models

Examples

  • 1. Physics-Informed Machine Learning (PIML)
  • 2. Operator Learning
  • 3. Plasma Simulation

Core Modules

  • 1. Core Module
  • 2. Physics-Informed Machine Learning (PIML)
  • 3. Operator Learning Module
  • 4. Plasma Physics Module
  • 5. Utilities

Development

  • 1. Contributing
ai4plasma
  • Examples
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Examples

This section contains example scripts and applications demonstrating the usage of AI4Plasma.

Categories

  • 1. Physics-Informed Machine Learning (PIML)
    • 1.1. Overview
    • 1.2. Standard PINN
    • 1.3. CS-PINN (Coefficient-Subnet PINN)
    • 1.4. RK-PINN (Runge-Kutta PINN)
    • 1.5. Meta-PINN
    • 1.6. Best Practices
    • 1.7. Performance Benchmarks
    • 1.8. Visualization and Monitoring
    • 1.9. Advanced Topics
    • 1.10. Troubleshooting Guide
    • 1.11. Further Reading
    • 1.12. Quick Start Guide
  • 2. Operator Learning
    • 2.1. Overview
    • 2.2. Common Setup
    • 2.3. DeepONet (Deep Operator Network)
    • 2.4. DeepCSNet (Deep Coefficient-Subnet Network)
    • 2.5. Best Practices
    • 2.6. Performance Benchmarks
    • 2.7. Troubleshooting
    • 2.8. References
    • 2.9. Further Reading
  • 3. Plasma Simulation
    • 3.1. Overview
    • 3.2. Arc Discharge Simulations
    • 3.3. Examples
    • 3.4. Best Practices
    • 3.5. Performance Benchmarks
    • 3.6. Troubleshooting Guide
    • 3.7. Advanced Topics
    • 3.8. Applications
    • 3.9. References
    • 3.10. Quick Start Guide

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