Induction and physical theory formation as well as universal computation by machine learning
General Physics
2021-05-20 v3
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.
Cite
@article{arxiv.1609.03862,
title = {Induction and physical theory formation as well as universal computation by machine learning},
author = {Alexander Svozil and Karl Svozil},
journal= {arXiv preprint arXiv:1609.03862},
year = {2021}
}
Comments
6 pages; added a paragraph on the simulation of UTMs by ml algorithms