»ã±¨±êÌâ (Title)£º·ÇÏßÐÔÂ˲¨Ëã·¨ÔÚ°¤´ÎÊý¾Ýͬ»¯ÖеÄÀûÓã¨The nonlinear filtering algorithms in sequential data assimilation£©

»ã±¨ÈË (Speaker)£ºÎÅÁÖ½Ü ½²Ê¦£¨ÖйúʯÓÍ´óѧ£¨±±¾©£©£©

»ã±¨¹¦·ò (Time)£º2024Äê8ÔÂ15ÈÕ (ÖÜËÄ) 10:00

»ã±¨µØÖ· (Place)£ºÐ£±¾²¿ GJ403

Ô¼ÇëÈË(Inviter)£º¼ÍÀö½à

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»ã±¨ÌáÒª£ºData assimilation is an essential task in the field of earth science in dealing with mathematical models of dynamical systems, and the filtering algorithm for nonlinear dynamical systems is a research hotspot in the field of data assimilation. In the first work we propose for modest dimensional filtering problems a defensive marginal PF algorithm which constructs a sampling distribution in the marginal space by combining the standard PF and the EnKF approximation using a multiple importance sampling (MIS) scheme. In the second work we propose transport mapping based variational ensemble Kalman filter for sequential Bayesian filtering problems with generic observation models, including affine-mapping and SVGD based mapping.