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Machine learning wall effects of eccentric spheres for convenient computation.

Lachlan J Gibson1, Shu Zhang1, Alexander B Stilgoe1

  • 1The University of Queensland, School of Mathematics and Physics, Brisbane QLD 4072, Australia.

Physical Review. E
|May 22, 2019
PubMed
Summary

This study quantifies wall effects on eccentric sphere motion within confined systems. A novel model combining analytical methods and artificial neural networks achieves high accuracy for spherical boundaries and infinite planes.

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Area of Science:

  • Physics
  • Biophysics
  • Computational Science

Background:

  • Confined systems, like biological cells, feature boundaries influencing internal particle dynamics.
  • Spherical particles and boundaries are common in biological and physical systems.
  • Analyzing non-concentric spherical interactions presents significant challenges.

Purpose of the Study:

  • To develop a method for quantifying wall effects on eccentric sphere motion within spherical boundaries.
  • To improve analytical techniques for evaluating these complex interactions.
  • To create an accurate computational model for predicting wall effects in various configurations.

Main Methods:

  • Enhancement of existing analytical methods for calculating wall effects.
  • Training a feed-forward artificial neural network (ANN) as part of a larger model.
  • Validation of the model across different spherical boundary conditions and extrapolation to planar boundaries.

Main Results:

  • The developed model demonstrates high accuracy, with errors around 0.001% within the training domain.
  • Extrapolation to an infinite plane (approximating a planar wall) yielded errors of approximately 0.05%.
  • The model efficiently determines wall effects for arbitrary internal sphere motion.

Conclusions:

  • The improved analytical and ANN-based model accurately quantifies wall effects in confined spherical systems.
  • This approach enables convenient and efficient analysis of experimentally achievable configurations.
  • The findings are applicable to diverse fields involving particle dynamics in confined environments.