We’ve all seen Physics-Informed Neural Networks (PINNs) that claim to solve fluid dynamics or control reactors. But usually, "Physics-Informed" just means "we added a penalty term to the loss function."

This is a problem. Soft constraints are not constraints; they are suggestions. If your Fusion Reactor AI sees a weird data outlier, a soft constraint might let it violate conservation of energy just to minimize the Mean Squared Error. In high-stakes fields (Nuclear, Medical, Robotics), this "hallucination" can be catastrophic.

i built LoureiroGate to fix this. It’s a PyTorch module that wraps your existing model and enforces Hard Limits based on input context using a differentiable gating mechanism.

It’s named after the late Nuno Loureiro (Plasma Physicist), whose work on the "Charge Starvation Limit" in relativistic plasmas inspired the architecture. We realized that if an AI doesn't know the speed of light limit, it predicts infinite energy in Magnetars. We generalized his math into a tool for everyone.

The repo includes a hardware-accelerated relativistic PIC engine (written in PyTorch) to demonstrate the physics.

Repo: https://github.com/Ashioya-ui/loureiro-gate

Happy to answer questions about plasma, gradients, or safety!

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