most citedCombining Dynamic Mode Decomposition with Ensemble Kalman Filtering for Tracking and Forecasting

14 citations · 14 across the 2 of their papers we have counts for

collaborators

2 papers

math.DS2024

Tracking and forecasting oscillatory data streams using Koopman autoencoders and Kalman filtering

Stephen A Falconer, David J. B. Lloyd, Naratip Santitissadeekorn

Data-driven modelling techniques provide a method for deriving models of dynamical systems directly from complicated data streams. However, tracking and forecasting such data strea…

math.DS2023★ 14 cited

Combining Dynamic Mode Decomposition with Ensemble Kalman Filtering for Tracking and Forecasting

Stephen A Falconer, David J. B. Lloyd, Naratip Santitissadeekorn

Data assimilation techniques, such as ensemble Kalman filtering, have been shown to be a highly effective and efficient way to combine noisy data with a mathematical model to track…