Randomized Residual-Based Error Estimators for Parametrized Equations

DOI10.1137/18M120364XzbMath1411.65148arXiv1807.10489OpenAlexW3102897257WikidataQ128146532 ScholiaQ128146532MaRDI QIDQ4631415

Kathrin Smetana, Olivier Zahm, Anthony T. Patera

Publication date: 29 March 2019

Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1807.10489

zbMATH Keywords

a posteriori error estimationconcentration phenomenongoal-oriented error estimationMonte Carlo estimatorprojection-based model order reductionparametrized equations

Mathematics Subject Classification ID

Analysis of algorithms and problem complexity (68Q25) Nonparametric tolerance and confidence regions (62G15) Monte Carlo methods (65C05) Error bounds for boundary value problems involving PDEs (65N15) Finite element, Rayleigh-Ritz and Galerkin methods for boundary value problems involving PDEs (65N30)

Related Items (9)

A probabilistic reduced basis method for parameter-dependent problems ⋮ Multi‐fidelity error estimation accelerates greedy model reduction of complex dynamical systems ⋮ Dual natural-norm a posteriori error estimators for reduced basis approximations to parametrized linear equations ⋮ Toward a certified greedy Loewner framework with minimal sampling ⋮ Probabilistic error estimation for non-intrusive reduced models learned from data of systems governed by linear parabolic partial differential equations ⋮ Machine-learning error models for approximate solutions to parameterized systems of nonlinear equations ⋮ Non-intrusive double-greedy parametric model reduction by interpolation of frequency-domain rational surrogates ⋮ Randomized linear algebra for model reduction. II: Minimal residual methods and dictionary-based approximation ⋮ On error estimation for reduced-order modeling of linear non-parametric and parametric systems

Uses Software

• redbKIT

Cites Work

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