抄録
In this paper we show a data assimilation framework which gives good estimation of the macroscopic parameter in particle systems. To give the fast and stable estimation, we employed the bounded Gaussian uniform mixture (BGUM) type dynamics that is originally introduced in the econophysics field. The result of the numerical experiment implies that BGUM type dynamics enable us to obtain appropriate estimation of macroscopic parameters from macroscopic observations faster than random walk type dynamics that is usually employed. It is also implied that mixing rate between Gaussian and uniform distribution can control the trade-off between fast detection and stability of macroscopic parameters. Those results suggest that the utility of the introduced framework for macroscopic parameter estimation in particle systems.
