Works (6)
Updated: July 5th, 2023 15:39
2019 journal article
Correcting observation model error in data assimilation
CHAOS, 29(5).
TL;DR:
This work proposes a method for observation model error correction within the filtering framework that involves an alternating minimization algorithm used to iteratively update a given observation function to increase consistency with the model and prior observations using ideas from attractor reconstruction.
(via Semantic Scholar)
arxiv.org (repository)
UN Sustainable Development Goal Categories
13. Climate Action
(Web of Science)
Source: Web Of Science
Added: July 22, 2019
2018 journal article
Nonlinear Kalman filtering for censored observations
Applied Mathematics and Computation, 316, 155–166.
author keywords: Extended Kalman filter; Censored observation; Parameter estimation; Hepatitis C virus (HCV); Human immunodeficiency virus (HIV)
TL;DR:
A modified version of the extended Kalman filter is developed to handle the case of censored observations in nonlinear systems and is validated in a simple oscillator system first, showing its ability to accurately reconstruct state variables and track system parameters when observations are censored.
(via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries, Crossref
Added: August 6, 2018
2017 journal article
Adaptive filtering for hidden node detection and tracking in networks
Chaos (Woodbury, N.Y.), 27(7).
Source: NC State University Libraries
Added: August 6, 2018
2017 journal article
Hybrid modeling and prediction of dynamical systems
PLoS Computational Biology, 13(7).
Source: NC State University Libraries
Added: August 6, 2018
2017 journal article
Kalman-Takens filtering in the presence of dynamical noise
EUROPEAN PHYSICAL JOURNAL-SPECIAL TOPICS, 226(15), 3239–3250.
TL;DR:
By combining the Kalman-Takens method with an adaptive filtering procedure, the statistics of the observational and dynamical noise are estimated and this solves a long-standing problem of separating dynamical and observational noise in time series data.
(via Semantic Scholar)
Source: Web Of Science
Added: August 6, 2018
2016 journal article
Ensemble Kalman Filtering without a Model
PHYSICAL REVIEW X, 6(1).
TL;DR:
A new kind of data analysis, free of assumptions from underlying models, is proposed and its use demonstrated on weather data.
(via Semantic Scholar)
Source: Web Of Science
Added: August 6, 2018
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