32 citations · 183 across the 54 of their papers we have counts for
4 papers · 1 filter
Continuous Data Assimilation with Learned Surrogate Dynamics
Wenwen Li, Daniel Sanz-Alonso
Continuous data assimilation seeks to estimate the state of a dynamical system from partial observations. In many applications, however, the state dynamics are unknown or prohibiti…
Long-time accuracy of ensemble Kalman filters for chaotic and machine-learned dynamical systems
Daniel Sanz-Alonso, Nathan Waniorek
Filtering is concerned with online estimation of the state of a dynamical system from partial and noisy observations. In applications where the state is high dimensional, ensemble…
Long-time Asymptotics of the Filtering Distribution for Partially Observed Chaotic Dynamical Systems
D. Sanz-Alonso, A. M. Stuart
The filtering distribution is a time-evolving probability distribution on the state of a dynamical system, given noisy observations. We study the large-time asymptotics of this pro…
Controlling Unpredictability with Observations in the Partially Observed Lorenz '96 Model
K. J. H. Law, D. Sanz-Alonso, A. Shukla +1
In the context of filtering chaotic dynamical systems it is well-known that partial observations, if sufficiently informative, can be used to control the inherent uncertainty due t…