Back to Search
Start Over
Characterizing Complex Spatiotemporal Patterns from Entropy Measures.
- Source :
- Entropy; Jun2024, Vol. 26 Issue 6, p508, 12p
- Publication Year :
- 2024
-
Abstract
- In addition to their importance in statistical thermodynamics, probabilistic entropy measurements are crucial for understanding and analyzing complex systems, with diverse applications in time series and one-dimensional profiles. However, extending these methods to two- and three-dimensional data still requires further development. In this study, we present a new method for classifying spatiotemporal processes based on entropy measurements. To test and validate the method, we selected five classes of similar processes related to the evolution of random patterns: (i) white noise; (ii) red noise; (iii) weak turbulence from reaction to diffusion; (iv) hydrodynamic fully developed turbulence; and (v) plasma turbulence from MHD. Considering seven possible ways to measure entropy from a matrix, we present the method as a parameter space composed of the two best separating measures of the five selected classes. The results highlight better combined performance of Shannon permutation entropy ( S H p ) and a new approach based on Tsallis Spectral Permutation Entropy ( S q s ). Notably, our observations reveal the segregation of reaction terms in this S H p × S q s space, a result that identifies specific sectors for each class of dynamic process, and it can be used to train machine learning models for the automatic classification of complex spatiotemporal patterns. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10994300
- Volume :
- 26
- Issue :
- 6
- Database :
- Complementary Index
- Journal :
- Entropy
- Publication Type :
- Academic Journal
- Accession number :
- 178154088
- Full Text :
- https://doi.org/10.3390/e26060508