Evaluation of dual-weighted residual and machine learning error estimation for projection-based reduced-order models of steady partial differential equations

Related Items (2)

Linearization errors in discrete goal-oriented error estimation ⋮ Efficient and accurate nonlinear model reduction via first-order empirical interpolation

Cites Work

Unnamed Item
Reduced basis approximation and a posteriori error estimation for affinely parametrized elliptic coercive partial differential equations. Application to transport and continuum mechanics.
Efficient model reduction in nonlinear dynamics using the Karhunen-Loève expansion and dual-weighted-residual methods
Galerkin v. least-squares Petrov-Galerkin projection in nonlinear model reduction
Anisotropic grid adaptation for functional outputs: application to two-dimensional viscous flows.
Comparison of some finite element methods for solving the diffusion-convection-reaction equation
Machine-learning error models for approximate solutions to parameterized systems of nonlinear equations
Efficient hyperreduction of high-order discontinuous Galerkin methods: element-wise and point-wise reduced quadrature formulations
Online adaptive basis refinement and compression for reduced-order models via vector-space sieving
Time-series machine-learning error models for approximate solutions to parameterized dynamical systems
Decay of the Kolmogorov \(N\)-width for wave problems
An optimization-based framework for anisotropic simplex mesh adaptation
A posteriori error estimation and basis adaptivity for reduced-basis approximation of nonaffine-parametrized linear elliptic partial differential equations
Adjoint sensitivity computations for an embedded-boundary Cartesian mesh method
Bayesian Calibration of Computer Models
A New Selection Operator for the Discrete Empirical Interpolation Method---Improved A Priori Error Bound and Extensions
A prioriconvergence of the Greedy algorithm for the parametrized reduced basis method
Reduced Basis Approximation for Nonlinear Parametrized Evolution Equations based on Empirical Operator Interpolation
The ROMES Method for Statistical Modeling of Reduced-Order-Model Error
Dimensional reduction of nonlinear finite element dynamic models with finite rotations and energy-based mesh sampling and weighting for computational efficiency
Adaptiveh-refinement for reduced-order models
On the stability and convergence of a Galerkin reduced order model (ROM) of compressible flow with solid wall and far-field boundary treatment
An optimal control approach to a posteriori error estimation in finite element methods
A Posteriori Error Analysis of the Reduced Basis Method for Nonaffine Parametrized Nonlinear PDEs
Efficient State/Parameter Estimation in Nonlinear Unsteady PDEs by a Reduced Basis Ensemble Kalman Filter
A posteriorierror bounds for reduced-basis approximations of parametrized parabolic partial differential equations
Reduced-basis output bound methods for parabolic problems

This page was built for publication: Evaluation of dual-weighted residual and machine learning error estimation for projection-based reduced-order models of steady partial differential equations