Machine learning for discovering laws of nature
This work addresses the challenge of automated scientific discovery for researchers in physics and AI, though it appears incremental as it rediscovers known laws rather than uncovering new ones.
The authors tackled the problem of discovering physical laws from raw data by developing 'machine scientists' that learn through a reward/punishment mechanism, resulting in the rediscovery of Newton's equation and Born's rule.
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).