Data-driven formulation of natural laws by recursive-LASSO-based symbolic regression
This addresses the challenge of automating the discovery of natural laws in science, potentially reducing reliance on human genius, though it appears incremental as it builds on existing symbolic regression techniques.
The authors tackled the problem of discovering natural laws from noisy data by proposing a recursive-LASSO-based symbolic regression method, which recurrently generates and selects features to construct highly nonlinear models, enabling data-driven formulation across various scientific fields.
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.