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# Statistically accurate low-order models for uncertainty quantification in turbulent dynamical systems.

@article{Sapsis2013StatisticallyAL, title={Statistically accurate low-order models for uncertainty quantification in turbulent dynamical systems.}, author={Themistoklis P. Sapsis and Andrew J. Majda}, journal={Proceedings of the National Academy of Sciences of the United States of America}, year={2013}, volume={110 34}, pages={13705-10} }

- Published 2013 in Proceedings of the National Academy of Sciences…
DOI:10.1073/pnas.1313065110

A framework for low-order predictive statistical modeling and uncertainty quantification in turbulent dynamical systems is developed here. These reduced-order, modified quasilinear Gaussian (ROMQG) algorithms apply to turbulent dynamical systems in which there is significant linear instability or linear nonnormal dynamics in the unperturbed system and energy-conserving nonlinear interactions that transfer energy from the unstable modes to the stable modes where dissipation occurs, resulting in… CONTINUE READING

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