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Physics driven behavioural clustering of free-falling paper shapes
- Source :
- PLoS ONE, Vol 14, Iss 6, p e0217997 (2019), PLoS ONE
- Publication Year :
- 2019
- Publisher :
- Public Library of Science (PLoS), 2019.
-
Abstract
- Many complex physical systems exhibit a rich variety of discrete behavioural modes. Often, the system complexity limits the applicability of standard modelling tools. Hence, understanding the underlying physics of different behaviours and distinguishing between them is challenging. Although traditional machine learning techniques could predict and classify behaviour well, typically they do not provide any meaningful insight into the underlying physics of the system. In this paper we present a novel method for extracting physically meaningful clusters of discrete behaviour from limited experimental observations. This method obtains a set of physically plausible functions that both facilitate behavioural clustering and aid in system understanding. We demonstrate the approach on the V-shaped falling paper system, a new falling paper type system that exhibits four distinct behavioural modes depending on a few morphological parameters. Using just 49 experimental observations, the method discovered a set of candidate functions that distinguish behaviours with an error of 2.04%, while also aiding insight into the physical phenomena driving each behaviour. © 2019 Howison et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
- Subjects :
- Inertia
Physiology
Physical system
Social Sciences
computer.software_genre
Systems Science
01 natural sciences
010305 fluids & plasmas
Physical Phenomena
Physical phenomena
Medicine and Health Sciences
Psychology
Cluster Analysis
Moment of Inertia
Multidisciplinary
Applied Mathematics
Simulation and Modeling
theoretical model
article
Classical Mechanics
Dynamical Systems
Variety (cybernetics)
Free falling
machine learning
Physical Sciences
Medicine
physics
Algorithms
Research Article
Paper
Computer and Information Sciences
Reynolds Number
Science
Fluid Mechanics
Research and Analysis Methods
Machine learning
Continuum Mechanics
Motion
Machine Learning Algorithms
Artificial Intelligence
0103 physical sciences
010306 general physics
Set (psychology)
Cluster analysis
Behavior
Biological Locomotion
business.industry
Biology and Life Sciences
Fluid Dynamics
Models, Theoretical
Nonlinear Dynamics
Artificial intelligence
business
computer
Mathematics
Subjects
Details
- ISSN :
- 19326203
- Volume :
- 14
- Database :
- OpenAIRE
- Journal :
- PLOS ONE
- Accession number :
- edsair.doi.dedup.....6e72a1d0c9a62a7f3b866e24f2a2d728