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  Stochastic goal-oriented error estimation with memory

Ackmann, J., Marotzke, J., & Korn, P. (2017). Stochastic goal-oriented error estimation with memory. Journal of Computational Physics, 348, 195-219. doi:10.1016/j.jcp.2017.07.009.

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 Creators:
Ackmann, Jan1, 2, Author           
Marotzke, Jochem3, Author                 
Korn, Peter2, Author           
Affiliations:
1IMPRS on Earth System Modelling, MPI for Meteorology, Max Planck Society, Bundesstraße 53, 20146 Hamburg, DE, ou_913547              
2Applied Mathematics and Computational Physics (AMCP), Scientific Computing Lab (ScLab), MPI for Meteorology, Max Planck Society, ou_2129636              
3Director’s Research Group OES, The Ocean in the Earth System, MPI for Meteorology, Max Planck Society, ou_913553              

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 Abstract: We propose a stochastic dual-weighted error estimator for the viscous shallow-water equation with boundaries. For this purpose, previous work on memory-less stochastic dual-weighted error estimation is extended by incorporating memory effects. The memory is introduced by describing the local truncation error as a sum of time-correlated random variables. The random variables itself represent the temporal fluctuations in local truncation errors and are estimated from high-resolution information at near-initial times. The resulting error estimator is evaluated experimentally in two classical ocean-type experiments, the Munk gyre and the flow around an island. In these experiments, the stochastic process is adapted locally to the respective dynamical flow regime. Our stochastic dual-weighted error estimator is shown to provide meaningful error bounds for a range of physically relevant goals. We prove, as well as show numerically, that our approach can be interpreted as a linearized stochastic-physics ensemble. © 2017 Elsevier Inc.

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Language(s): eng - English
 Dates: 2016-0220172017-082017-11
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.jcp.2017.07.009
 Degree: -

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Title: Journal of Computational Physics
Source Genre: Journal
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Publ. Info: Amsterdam : Elsevier B.V.
Pages: - Volume / Issue: 348 Sequence Number: - Start / End Page: 195 - 219 Identifier: ISSN: 0021-9991
CoNE: https://pure.mpg.de/cone/journals/resource/954922645031