Show HN: System that rediscovers physics laws from raw data autonomously(protoscience.ai)
protoscience.ai
Show HN: System that rediscovers physics laws from raw data autonomously
https://protoscience.ai
2 comments
The BH series is wild. Rediscovering GR relations from observables alone with R²=1.000. No physics priors, just raw data. That's the part that got me.
Exactly — that was the surprising part for me too.
The system has no notion of “physics” at all — it’s just searching for compressible structure in the data.
The fact that GR relations emerge from observables suggests that a lot of what we call “laws” might just be the simplest compressions of measurement space.
Still early, but curious how far this goes.
The system has no notion of “physics” at all — it’s just searching for compressible structure in the data.
The fact that GR relations emerge from observables suggests that a lot of what we call “laws” might just be the simplest compressions of measurement space.
Still early, but curious how far this goes.
It does not use LLMs for discovery — only sparse regression, power-law fitting, and statistical validation.
Results so far:
- Kepler's Third Law (P² = a³ / M) from 3,519 NASA exoplanets — R² = 0.998 - Sun’s ~27-day rotation period from solar wind plasma data — 93% accuracy - Power law T ~ v^3.40 in solar wind (NOAA/NASA spacecraft data) - 5/5 General Relativity predictions from simulated black hole observables — all R² = 1.000 - Chirp mass relationship from 219 LIGO gravitational wave events — R² = 0.998
It also detects when no meaningful law exists — Bitcoin daily prices returned R² = 0.00.
Pipeline:
raw data → feature extraction → candidate law generation → fitting → verification
An LLM (Claude) is only used at the end to interpret results in natural language — it is never involved in the discovery step.
All experiments are fully reproducible.
Code: https://github.com/SaulVanCode/protoscience-nasa-experiments