Dynamic Stochastic Superresolution of sparsely observed turbulent systems
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Publication:346371
DOI10.1016/j.jcp.2012.11.037zbMath1349.76761OpenAlexW2127921550MaRDI QIDQ346371
Andrew J. Majda, Michal Branicki
Publication date: 5 December 2016
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://www.pure.ed.ac.uk/ws/files/18236445/Dynamic_Stochastic_Superresolution_of_sparsely_observed_turbulent_systems.pdf
Stochastic analysis applied to problems in fluid mechanics (76M35) Particle methods and lattice-gas methods (76M28) Turbulence (76Fxx) Meteorology and atmospheric physics (86A10) Computational methods for problems pertaining to geophysics (86-08)
Related Items (16)
Hierarchical sparse observation models and informative prior for Bayesian inference of spatially varying parameters ⋮ Learning nonlinear turbulent dynamics from partial observations via analytically solvable conditional statistics ⋮ New perspectives on superparameterization for geophysical turbulence ⋮ An efficient data-driven multiscale stochastic reduced order modeling framework for complex systems ⋮ Predicting observed and hidden extreme events in complex nonlinear dynamical systems with partial observations and short training time series ⋮ Efficient statistically accurate algorithms for the Fokker-Planck equation in large dimensions ⋮ Rigorous Analysis for Efficient Statistically Accurate Algorithms for Solving Fokker--Planck Equations in Large Dimensions ⋮ A new efficient parameter estimation algorithm for high-dimensional complex nonlinear turbulent dynamical systems with partial observations ⋮ Accuracy of Some Approximate Gaussian Filters for the Navier--Stokes Equation in the Presence of Model Error ⋮ Stochastic Superparameterization and Multiscale Filtering of Turbulent Tracers ⋮ Ensemble Kalman filters for dynamical systems with unresolved turbulence ⋮ Efficient nonlinear optimal smoothing and sampling algorithms for complex turbulent nonlinear dynamical systems with partial observations ⋮ Reduced one-dimensional models for wave turbulence system ⋮ Improving the prediction of complex nonlinear turbulent dynamical systems using nonlinear filter, smoother and backward sampling techniques ⋮ An information-theoretic framework for improving imperfect dynamical predictions via multi-model ensemble forecasts ⋮ Recovering the Eulerian energy spectrum from noisy Lagrangian tracers
Uses Software
Cites Work
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