A deeper look into natural sciences with physics-based and data-driven measures

Davi Röhe Rodrigues; Karin Everschor-Sitte; Susanne Gerber; Illia Horenko

doi:10.1016/j.isci.2021.102171

A deeper look into natural sciences with physics-based and data-driven measures

iScience. 2021 Feb 9;24(3):102171. doi: 10.1016/j.isci.2021.102171. eCollection 2021 Mar 19.

Authors

Davi Röhe Rodrigues¹, Karin Everschor-Sitte¹, Susanne Gerber², Illia Horenko³

Affiliations

¹ Institute of Physics, Johannes Gutenberg University of Mainz, 55128 Mainz, Germany.
² Institute of Human Genetics, University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany.
³ Università della Svizzera Italiana, Faculty of Informatics, Via G. Buffi 13, 6900 Lugano, Switzerland.

Abstract

With the development of machine learning in recent years, it is possible to glean much more information from an experimental data set to study matter. In this perspective, we discuss some state-of-the-art data-driven tools to analyze latent effects in data and explain their applicability in natural science, focusing on two recently introduced, physics-motivated computationally cheap tools-latent entropy and latent dimension. We exemplify their capabilities by applying them on several examples in the natural sciences and show that they reveal so far unobserved features such as, for example, a gradient in a magnetic measurement and a latent network of glymphatic channels from the mouse brain microscopy data. What sets these techniques apart is the relaxation of restrictive assumptions typical of many machine learning models and instead incorporating aspects that best fit the dynamical systems at hand.

Keywords: Applied Physics; Artificial Intelligence; Computer Science; Magnetism; Physics.

Publication types

Review