Rodrigues, D.R. and Everschor-Sitte, K. and Gerber, S. and Horenko, I.
(2021)
*A deeper look into natural sciences with physics-based and data-driven measures.*
iSCIENCE, 24
(3).
pp. 1-13.
ISSN 102171

Full text not available from this repository.

Official URL: https://doi.org/10.1016/j.isci.2021.102171

## 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.

Item Type: | Article |
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Subjects: | Mathematical and Computer Sciences > Mathematics > Applied Mathematics |

Divisions: | Department of Mathematics and Computer Science > Institute of Mathematics |

ID Code: | 2555 |

Deposited By: | Monika Drueck |

Deposited On: | 27 Apr 2021 12:19 |

Last Modified: | 27 Apr 2021 12:19 |

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