[M-GI28] Data-driven geosciences

[MGI28-P05] Data-driven estimation of spatiotemporal nonlinear dynamics of heterogeneous reaction

Takeshi Aibe¹, Tatsu Kuwatani², Ryosuke Oyanagi³, *Toshiaki Omori¹ (1.Kobe University, 2.JAMSTEC, 3.Kokushikan University)

Keywords:Data-driven Science, Heterogeneous reactions, Nonlinear dynamics, Machine learning

Heterogeneous reactions are chemical reactions that occur at the interfaces of multiple phases, and often show a nonlinear dynamical behavior due to the effect of the time-variant surface area with complex reaction mechanisms. It is essential to specify the kinetics of heterogeneous reactions in order to elucidate the microscopic elementary processes and predict the macroscopic future evolution of the system. In this study, we propose a data-driven method for simultaneously extracting spatiotemporal dynamics of heterogeneous reactions and spatial distribution of underlying parameters of governing equations by using partial observation data. The results using the proposed method show that the non-uniform distribution of diffusion constants and the rate constants of dissolution and precipitation reactions as well as spatiotemporal changes of solid reactants and products were successfully estimated from only observable spatiotemporal changes in the concentration of the dissolved intermediate products.