NAKANO NAOTO    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.