Identifying generative mechanisms from spatiotemporal patterns in diffusion phenomena

Authors:
Takuya Ueda;Yoshiteru Ishida
Affiliations:
Department of Electrical and Information Engineering, Toyohashi University of Technology, Toyohashi, Japan;Department of Computer Science and Engineering, Toyohashi University of Technology, Toyohashi, Japan
Venue:
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part IV
Year:
2011

Citing 2
Cited 0

Extracting cellular automaton rules directly from experimental data

Physica D
Theory of Self-Reproducing Automata

Theory of Self-Reproducing Automata

Quantified Score

Hi-index	0.00

Visualization

Abstract

Various phenomena such as natural and social ones present us inexhaustible amount of spatial patterns. There are many studies for physical and mathematical models to understand these phenomena. On the other hand, inverse problems can be posed for these studies. An earlier work studied an identification method of generative mechanisms from spatiotemporal patterns as cellular automata (CA) rules. This note applies the identification method to spatiotemporal patterns generated by real diffusion phenomena. The effectiveness of the identifying method is evaluated for the real diffusion phenomena.