---
license: mit
tags:
  - scientific-machine-learning
  - ai-for-science
  - physics-informed-machine-learning
  - agentic-ai
  - boiling
  - phase-change
---

# Boiling Intelligence

A minimal prototype of an autonomous AI physicist for phase-change mass-transfer discovery.

## v0.4

The current synthetic benchmark demonstrates a closed scientific loop:

1. Maintain competing mass-transfer closures.
2. Design discriminating tests.
3. Query a hidden synthetic physical world.
4. Update evidence.
5. Detect model-class failure.
6. Infer a minimal constitutive correction.
7. Compile the correction into a new executable model.
8. Re-enter the revised model into the falsification loop.

### Example discovered closure

\[
M_3 = 0.8\,\Delta T + 0.00196515\,\Delta T^2
\]

The hidden synthetic law is used only to validate the autonomous discovery architecture.

**This release does not claim discovery of a real boiling constitutive law.**

## Run

```bash
pip install -r requirements.txt
ollama serve
ollama pull gpt-oss:20b
python main.py
```

## Roadmap

- Agentic scientific reasoning
- Conservative multiscale recurrent neural operators
- Basilisk phase-change simulations
- Experimental ground-truth integration
- Closed-loop physical model discovery