Franz W. Hamilton

Works (6)

Updated: July 5th, 2023 15:39

2019 journal article

Correcting observation model error in data assimilation

CHAOS, 29(5).

By: F. Hamilton n, T. Berry * & T. Sauer *

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)
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13. Climate Action (Web of Science)
Source: Web Of Science
Added: July 22, 2019

2017 journal article

Adaptive filtering for hidden node detection and tracking in networks

Chaos (Woodbury, N.Y.), 27(7).

By: F. Hamilton, B. Setzer, S. Chavez, H. Tran  & A. Lloyd 

Source: NC State University Libraries
Added: August 6, 2018

2017 journal article

Hybrid modeling and prediction of dynamical systems

PLoS Computational Biology, 13(7).

By: F. Hamilton, A. Lloyd  & K. Flores

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.

By: F. Hamilton n, T. Berry * & T. Sauer *

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

2017 journal article

Nonlinear Kalman filtering for censored observations

Applied Mathematics and Computation, 316, 155–166.

By: J. Arthur *, A. Attarian*, F. Hamilton n & H. Tran n 

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

2016 journal article

Ensemble Kalman Filtering without a Model

PHYSICAL REVIEW X, 6(1).

By: F. Hamilton n, T. Berry * & T. Sauer *

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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