A multi-fidelity ensemble Kalman filter with hyperreduced reduced-order models

DOI10.1016/j.cma.2022.115282OpenAlexW4284977277MaRDI QIDQ2160470

Geoff Donoghue, Masayuki Yano

Publication date: 3 August 2022

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cma.2022.115282

zbMATH Keywords

data assimilationaerodynamicsuncertainty quantificationensemble filteringmulti-fidelity Monte Carlononlinear hyperreduction

Mathematics Subject Classification ID

Monte Carlo methods (65C05) Probabilistic methods, particle methods, etc. for initial value and initial-boundary value problems involving PDEs (65M75)

Related Items (2)

A novel estimation method for microstructural evolution based on data assimilation and phase field crystal model ⋮ A reduced basis ensemble Kalman method

Uses Software

• EnKF

Cites Work

• Unnamed Item
• Unnamed Item
• Reduced basis approximation and a posteriori error estimation for affinely parametrized elliptic coercive partial differential equations. Application to transport and continuum mechanics.
• A new finite element formulation for computational fluid dynamics. I: Symmetric forms of the compressible Euler and Navier-Stokes equations and the second law of thermodynamics
• On the symmetric form of systems of conservation laws with entropy
• Parameterised non-intrusive reduced order methods for ensemble Kalman filter data assimilation
• An `empirical interpolation' method: Application to efficient reduced-basis discretization of partial differential equations
• An LP empirical quadrature procedure for reduced basis treatment of parametrized nonlinear PDEs
• Goal-oriented model reduction for parametrized time-dependent nonlinear partial differential equations
• Reduced basis approximation and a posteriori error bounds for 4D-Var data assimilation
• 3D-VAR for parameterized partial differential equations: a certified reduced basis approach
• A model and variance reduction method for computing statistical outputs of stochastic elliptic partial differential equations
• Multilevel Ensemble Transform Particle Filtering
• A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems
• Multilevel ensemble Kalman filtering
• Accelerating Optimization of Parametric Linear Systems by Model Order Reduction
• Efficient Characterization of Uncertain Model Parameters with a Reduced-Order Ensemble Kalman Filter
• Efficient non-linear model reduction via a least-squares Petrov-Galerkin projection and compressive tensor approximations
• Multilevel Monte Carlo Methods
• Dimensional reduction of nonlinear finite element dynamic models with finite rotations and energy-based mesh sampling and weighting for computational efficiency
• Progressive construction of a parametric reduced‐order model for PDE‐constrained optimization
• A reduced basis Kalman filter for parametrized partial differential equations
• Nonlinear Model Reduction via Discrete Empirical Interpolation
• An Entropy Adjoint Approach to Mesh Refinement
• Multilevel Monte Carlo Path Simulation
• Newton-GMRES Preconditioning for Discontinuous Galerkin Discretizations of the Navier–Stokes Equations
• GMRES: A Generalized Minimal Residual Algorithm for Solving Nonsymmetric Linear Systems
• Diagonally Implicit Runge–Kutta Methods for Stiff O.D.E.’s
• Multilevel Particle Filters
• Efficient State/Parameter Estimation in Nonlinear Unsteady PDEs by a Reduced Basis Ensemble Kalman Filter
• Multilevel Monte Carlo Covariance Estimation for the Computation of Sobol' Indices
• A Multifidelity Ensemble Kalman Filter with Reduced Order Control Variates
• Data Assimilation
• The ensemble Kalman filter for combined state and parameter estimation

This page was built for publication: A multi-fidelity ensemble Kalman filter with hyperreduced reduced-order models