Improving the prediction of complex nonlinear turbulent dynamical systems using nonlinear filter, smoother and backward sampling techniques
From MaRDI portal
Publication:783088
DOI10.1007/s40687-020-00216-5zbMath1444.62102OpenAlexW3040729469MaRDI QIDQ783088
Publication date: 30 July 2020
Published in: Research in the Mathematical Sciences (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s40687-020-00216-5
Inference from stochastic processes and prediction (62M20) Estimation in multivariate analysis (62H12) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Linear regression; mixed models (62J05) Statistics of extreme values; tail inference (62G32) Applications of statistics to physics (62P35) Geostatistics (86A32) Statistical aspects of big data and data science (62R07)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- An introduction to turbulent dynamical systems in complex systems
- Predicting fat-tailed intermittent probability distributions in passive scalar turbulence with imperfect models through empirical information theory
- Dynamic Stochastic Superresolution of sparsely observed turbulent systems
- An ensemble Kalman filter for statistical estimation of physics constrained nonlinear regression models
- New perspectives on superparameterization for geophysical turbulence
- Quantifying Bayesian filter performance for turbulent dynamical systems through information theory
- Information theory and dynamical system predictability
- Proper orthogonal decomposition closure models for turbulent flows: a numerical comparison
- Efficient nonlinear optimal smoothing and sampling algorithms for complex turbulent nonlinear dynamical systems with partial observations
- Test models for improving filtering with model errors through stochastic parameter estimation
- Improving filtering and prediction of spatially extended turbulent systems with model errors through stochastic parameter estimation
- Nonlinear prediction of chaotic time series
- Higher-order implicit strong numerical schemes for stochastic differential equations
- A reduced-order method for simulation and control of fluid flows
- Low-dimensional reduced-order models for statistical response and uncertainty quantification: barotropic turbulence with topography
- Efficient statistically accurate algorithms for the Fokker-Planck equation in large dimensions
- Control of the Burgers equation by a reduced-order approach using proper orthogonal decomposition
- Information theory, model error, and predictive skill of stochastic models for complex nonlinear systems
- Learning nonlinear turbulent dynamics from partial observations via analytically solvable conditional statistics
- Noisy Lagrangian tracers for filtering random rotating compressible flows
- Predicting the cloud patterns for the boreal summer intraseasonal oscillation through a low-order stochastic model
- Blending modified Gaussian closure and non-Gaussian reduced subspace methods for turbulent dynamical systems
- Quantification and prediction of extreme events in a one-dimensional nonlinear dispersive wave model
- Challenges in Climate Science and Contemporary Applied Mathematics
- Quantifying uncertainty for predictions with model error in non-Gaussian systems with intermittency
- Information barriers for noisy Lagrangian tracers in filtering random incompressible flows
- An MCMC Algorithm for Parameter Estimation in Signals with Hidden Intermittent Instability
- Blended particle filters for large-dimensional chaotic dynamical systems
- Normal forms for reduced stochastic climate models
- High skill in low-frequency climate response through fluctuation dissipation theorems despite structural instability
- An iterative method for solution of the likelihood equations for incomplete data from exponential families
- Models for stochastic climate prediction
- Data-Driven Filtered Reduced Order Modeling of Fluid Flows
- Strategies for Reduced-Order Models for Predicting the Statistical Responses and Uncertainty Quantification in Complex Turbulent Dynamical Systems
- Atmospheric and Oceanic Fluid Dynamics
- Deterministic Nonperiodic Flow
- Rigorous Analysis for Efficient Statistically Accurate Algorithms for Solving Fokker--Planck Equations in Large Dimensions
- Beating the curse of dimension with accurate statistics for the Fokker–Planck equation in complex turbulent systems
- Improving model fidelity and sensitivity for complex systems through empirical information theory
- Link between statistical equilibrium fidelity and forecasting skill for complex systems with model error
- Physics constrained nonlinear regression models for time series
- Predicting observed and hidden extreme events in complex nonlinear dynamical systems with partial observations and short training time series
- Data Assimilation
- The ensemble Kalman filter for combined state and parameter estimation
- Filtering Complex Turbulent Systems
- An applied mathematics perspective on stochastic modelling for climate
- Prediction of Turbulent Flows
- On Information and Sufficiency
- Handbook of stochastic methods for physics, chemistry and the natural sciences.
- Data Assimilation
This page was built for publication: Improving the prediction of complex nonlinear turbulent dynamical systems using nonlinear filter, smoother and backward sampling techniques
