A causality-based learning approach for discovering the underlying dynamics of complex systems from partial observations with stochastic parameterization

Nonlinear systems in control theory (93C10) Estimation and detection in stochastic control theory (93E10) Stochastic learning and adaptive control (93E35)

Cites Work

Unnamed Item
Unnamed Item
Unnamed Item
Unnamed Item
Unnamed Item
Discovering governing equations from data by sparse identification of nonlinear dynamical systems
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
An ensemble Kalman filter for statistical estimation of physics constrained nonlinear regression models
New perspectives on superparameterization for geophysical turbulence
Stochastic superparameterization in quasigeostrophic turbulence
Multi-level dynamical systems: connecting the Ruelle response theory and the Mori-Zwanzig approach
Lessons in uncertainty quantification for turbulent dynamical systems
Information theory and dynamical system predictability
Stochastic superparameterization in a one-dimensional model for wave turbulence
Efficient nonlinear optimal smoothing and sampling algorithms for complex turbulent nonlinear dynamical systems with partial observations
Data-driven POD-Galerkin reduced order model for turbulent flows
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
Topics in geophysical fluid dynamics: atmospheric dynamics, dynamo theory, and climate dynamics
Stochastic resonance in neuron models
Data-driven non-Markovian closure models
Dynamic data-driven reduced-order models
Non-Gaussian test models for prediction and state estimation with model errors
Data-driven variational multiscale reduced order models
Sparse identification of nonlinear dynamical systems via reweighted \(\ell_1\)-regularized least squares
Supervised learning from noisy observations: combining machine-learning techniques with data assimilation
Data-driven model reduction, Wiener projections, and the Koopman-Mori-Zwanzig formalism
Learning nonlinear turbulent dynamics from partial observations via analytically solvable conditional statistics
Exploring the need for localization in ensemble data assimilation using a hierarchical ensemble filter
Exact simulation of diffusions
A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems
Extremes and Recurrence in Dynamical Systems
Quantifying uncertainty for predictions with model error in non-Gaussian systems with intermittency
Directed Information Graphs
Mathematical test models for superparametrization in anisotropic turbulence
Linear Inversion of Band-Limited Reflection Seismograms
The Calculation of Posterior Distributions by Data Augmentation
Extracting Sparse High-Dimensional Dynamics from Limited Data
Atmospheric and Oceanic Fluid Dynamics
Accuracy of Some Approximate Gaussian Filters for the Navier--Stokes Equation in the Presence of Model Error
Data-adaptive harmonic spectra and multilayer Stuart-Landau models
Beating the curse of dimension with accurate statistics for the Fokker–Planck equation in complex turbulent systems
A mathematical framework for stochastic climate models
Physics constrained nonlinear regression models for time series
Using data assimilation to train a hybrid forecast system that combines machine-learning and knowledge-based components
Reduced-order models for coupled dynamical systems: Data-driven methods and the Koopman operator
Learning stochastic closures using ensemble Kalman inversion
The role of additive and multiplicative noise in filtering complex dynamical systems
The ensemble Kalman filter for combined state and parameter estimation
Filtering Complex Turbulent Systems
Information Theory and Stochastics for Multiscale Nonlinear Systems
On Information and Sufficiency
Statistics of Extreme Events in Fluid Flows and Waves
Handbook of stochastic methods for physics, chemistry and the natural sciences.
Data Assimilation