Department   Graduate School  , Graduate School of Advanced Mathematical Sciences
   Position   Associate Professor (non-tenured)
Research Period 2016/04~2019/03
Research Topic Cross-cutting research of data- and model-driven methods by interlacing deductive and inductive cellular automata constructing method
Research Type KAKENHI Research
Consignor Japan Society for the Promotion of Science
Research Program Type Grant-in-Aid for Challenging Exploratory Research
KAKENHI Grant No. 16K13772
Responsibility Representative Researcher
Representative Person NAKANO Naoto
Details In this research, we studied empirical cellular automata (CA) construction method as a new modeling method for phenomena. We set the following two approaches to establish the empirical CA construction method: (A) numerical analysis on the selectivity of local rules of CA; (B) refinement of methodology for quantitative modelling. In (A), we investigated the relationship between solutions of CAs and PDEs by the use of interval operation and found the selection tendencies of resultant local rules of empirical CA mathematically. In (B), we constructed a model that mimics the solution behavior of nonlinear wave phenomena in a data driven manner. Furthermore, investigating the connection of our method with machine learning techniques and the method for analysis of global dynamics, we also obtained a novel method of modelling phenomenon.