# Machine learning for discovering laws of nature

arXiv: https://arxiv.org/abs/2303.17607 · HTML: https://mlsanity.com/paper/2303.17607/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 35 references that are in the map.
- First posted on arXiv 18 Mar 2023.

## Builds on (most cited first, 4 of 4)

- [AI Feynman: a Physics-Inspired Method for Symbolic Regression](https://mlsanity.com/paper/1905.11481/) (2019)
- [Rediscovering orbital mechanics with machine learning](https://mlsanity.com/paper/2202.02306/) (2022)
- [On scientific understanding with artificial intelligence](https://mlsanity.com/paper/2204.01467/) (2022)
- [A Bayesian machine scientist to aid in the solution of challenging scientific problems](https://mlsanity.com/paper/2004.12157/) (2020)

## Abstract (arXiv)

Based on Darwin's natural selection, we developed "machine scientists" to discover the laws of nature by learning from raw data. "Machine scientists" construct physical theories by applying a logic tree (state Decision Tree) and a value tree (observation Function Tree); the logical tree determines the state of the entity, and the value tree determines the absolute value between the two observations of the entity. A logic Tree and a value tree together can reconstruct an entity's trajectory and make predictions about its future outcomes. Our proposed algorithmic model has an emphasis on machine learning - where "machine scientists" builds up its experience by being rewarded or punished for each decision they make - eventually leading to rediscovering Newton's equation (classical physics) and the Born's rule (quantum mechanics).