Induction and physical theory formation as well as universal computation by machine learning
arXiv:1609.03862 · doi:10.1201/9780429028618
Abstract
Machine learning presents a general, systematic framework for the generation of formal theoretical models for physical description and prediction. Tentatively standard linear modeling techniques are reviewed; followed by a brief discussion of generalizations to deep forward networks for approximating nonlinear phenomena and universal computers.
6 pages; added a paragraph on the simulation of UTMs by ml algorithms