Document Type
Presentation
Location
Math Conference Room: College of Arts and Sciences
Start Date
4-11-2025 10:00 AM
End Date
4-11-2025 11:00 AM
Description
In this talk, we present a hybrid data assimilation (DA) method that integrates continuous data assimilation (CDA) with particle filtering to estimate parameters in dynamical systems. Parameter estimation in such systems is particularly challenging because it involves both determining the parameters and estimating the often high-dimensional physical state. To address this difficulty, we decouple the estimation of states and parameters by employing CDA for state estimation and particle filtering for parameter estimation, with information exchanged alternately between the two. This hybrid framework leverages the strengths of CDA in handling high-dimensional state estimation and the efficiency of particle filters in estimating low-dimensional parameters. Numerical experiments demonstrate the effectiveness of the proposed approach.
A Hybrid Data Assimilation Approach for Parameter Estimation in Dynamical Systems
Math Conference Room: College of Arts and Sciences
In this talk, we present a hybrid data assimilation (DA) method that integrates continuous data assimilation (CDA) with particle filtering to estimate parameters in dynamical systems. Parameter estimation in such systems is particularly challenging because it involves both determining the parameters and estimating the often high-dimensional physical state. To address this difficulty, we decouple the estimation of states and parameters by employing CDA for state estimation and particle filtering for parameter estimation, with information exchanged alternately between the two. This hybrid framework leverages the strengths of CDA in handling high-dimensional state estimation and the efficiency of particle filters in estimating low-dimensional parameters. Numerical experiments demonstrate the effectiveness of the proposed approach.