ORCID Profile
0000-0002-5533-0838
Current Organisation
University of Adelaide
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: American Institute of Mathematical Sciences (AIMS)
Date: 2021
DOI: 10.3934/FODS.2021019
Abstract: style='text-indent:20px ' Many recent advances in sequential assimilation of data into nonlinear high-dimensional models are modifications to particle filters which employ efficient searches of a high-dimensional state space. In this work, we present a complementary strategy that combines statistical emulators and particle filters. The emulators are used to learn and offer a computationally cheap approximation to the forward dynamic mapping. This emulator-particle filter (Emu-PF) approach requires a modest number of forward-model runs, but yields well-resolved posterior distributions even in non-Gaussian cases. We explore several modifications to the Emu-PF that utilize mechanisms for dimension reduction to efficiently fit the statistical emulator, and present a series of simulation experiments on an atypical Lorenz-96 system to demonstrate their performance. We conclude with a discussion on how the Emu-PF can be paired with modern particle filtering algorithms.
Publisher: Society for Industrial & Applied Mathematics (SIAM)
Date: 08-2022
DOI: 10.1137/21M1437172
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Informa UK Limited
Date: 29-11-2021
Publisher: Elsevier BV
Date: 11-2015
Publisher: Elsevier BV
Date: 06-2022
Publisher: Society for Industrial & Applied Mathematics (SIAM)
Date: 2015
DOI: 10.1137/140982374
Publisher: Elsevier BV
Date: 12-2017
Publisher: Springer Science and Business Media LLC
Date: 30-10-2021
Publisher: International Press of Boston
Date: 2014
Publisher: Wiley
Date: 16-03-2021
DOI: 10.1002/QJ.4001
Abstract: We introduce a framework for data assimilation (DA) in which the data is split into multiple sets corresponding to low‐rank projections of the state space. Algorithms are developed that assimilate some or all of the projected data, including an algorithm compatible with any generic DA method. The major application explored here is PROJ‐PF, a projected particle filter. The PROJ‐PF implementation assimilates highly informative but low‐dimensional observations. The implementation considered here is based on using projections corresponding to assimilation in the unstable subspace (AUS). In the context of particle filtering, the projected approach mitigates the collapse of particle ensembles in high‐dimensional DA problems, while preserving as much relevant information as possible, as the unstable and neutral modes correspond to the most uncertain model predictions. In particular, we formulate and implement numerically a projected optimal proposal particle filter (PROJ‐OP‐PF) and compare this with the standard optimal proposal and the ensemble transform Kalman filter.
Location: United States of America
No related grants have been discovered for John Maclean.