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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 |