RL-PINNs: A PINN Framework with an RL Brain

RL-PINNs is designed as a creative reinforcement-learning framework for Physics-Informed Neural Networks. The idea is simple: treat training decisions like a strategy game, so PINNs can adapt their learning behavior instead of staying static.

Framework Intent

This project focuses on framework design and practical control loops around PINN training, not a static one-shot pipeline.

RL-guided decision flow for PINN optimization behavior.
Designed to be modular for experimentation and extension.
Built to evolve with more policies, evaluators, and training recipes.

System Architecture

High-level RL-PINNs flow from PDE setup and PINN solving to the RL-driven adaptation loop.

RL-PINNs architecture diagram showing high-level pipeline and RL environment control loop. — Figure: RL-PINNs architecture and training-control loop.

What’s Next

More case studies, metrics, and model evolution snapshots will be added as the framework presentation evolves.

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