Data-driven formulation of natural laws by recursive-LASSO-based symbolic regression
Data Analysis, Statistics and Probability
2021-02-19 v1 Machine Learning
Machine Learning
Abstract
Discovery of new natural laws has for a long time relied on the inspiration of some genius. Recently, however, machine learning technologies, which analyze big data without human prejudice and bias, are expected to find novel natural laws. Here we demonstrate that our proposed machine learning, recursive-LASSO-based symbolic (RLS) regression, enables data-driven formulation of natural laws from noisy data. The RLS regression recurrently repeats feature generation and feature selection, eventually constructing a data-driven model with highly nonlinear features. This data-driven formulation method is quite general and thus can discover new laws in various scientific fields.
Cite
@article{arxiv.2102.09210,
title = {Data-driven formulation of natural laws by recursive-LASSO-based symbolic regression},
author = {Yuma Iwasaki and Masahiko Ishida},
journal= {arXiv preprint arXiv:2102.09210},
year = {2021}
}