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

Davi Röhe Rodrigues1, Karin Everschor-Sitte1, Susanne Gerber2

  • 1Institute of Physics, Johannes Gutenberg University of Mainz, 55128 Mainz, Germany.

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Summary
This summary is machine-generated.

New data-driven tools, latent entropy and latent dimension, analyze complex scientific data. These physics-motivated methods reveal hidden patterns in magnetic measurements and mouse brain imaging, offering novel insights into natural sciences.

Keywords:
Applied PhysicsArtificial IntelligenceComputer ScienceMagnetismPhysics

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Area of Science:

  • Natural Sciences
  • Data Science
  • Physics

Background:

  • Machine learning advancements enable deeper analysis of experimental data.
  • Traditional models often rely on restrictive assumptions.
  • Analyzing latent effects in data is crucial for scientific discovery.

Purpose of the Study:

  • To discuss state-of-the-art data-driven tools for analyzing latent effects.
  • To explain the applicability of these tools in natural sciences.
  • To highlight novel, computationally cheap, physics-motivated methods: latent entropy and latent dimension.

Main Methods:

  • Focus on two recently introduced tools: latent entropy and latent dimension.
  • These methods relax restrictive assumptions common in machine learning.
  • The tools incorporate aspects that best fit dynamical systems.

Main Results:

  • Demonstrated applicability on several natural science examples.
  • Revealed unobserved features, including a magnetic measurement gradient.
  • Identified a latent network of glymphatic channels in mouse brain microscopy data.

Conclusions:

  • Latent entropy and latent dimension are powerful, flexible tools for data analysis.
  • These methods offer new perspectives in natural sciences by uncovering hidden data features.
  • The physics-motivated approach provides computationally efficient and insightful analysis.