Model order reduction for parametrized nonlinear hyperbolic problems as an application to uncertainty quantification

DOI10.1016/j.cam.2018.09.018zbMath1404.65112arXiv1808.03311OpenAlexW2887919336WikidataQ129208570 ScholiaQ129208570MaRDI QIDQ1757391

R. Crisovan, Svetlana Tokareva, Davide Torlo, Remi Abgrall

Publication date: 4 January 2019

Published in: Journal of Computational and Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1808.03311

zbMATH Keywords

reduced order modelingempirical interpolation methodresidual distributionUQnonlinear hyperbolic problemsPOD-Greedy

Mathematics Subject Classification ID

Shock waves and blast waves in fluid mechanics (76L05) Hyperbolic conservation laws (35L65) Error bounds for initial value and initial-boundary value problems involving PDEs (65M15) PDEs with randomness, stochastic partial differential equations (35R60) Numerical solutions to equations with nonlinear operators (65J15) Finite volume methods for initial value and initial-boundary value problems involving PDEs (65M08)

Related Items (9)

The neural network shifted-proper orthogonal decomposition: a machine learning approach for non-linear reduction of hyperbolic equations ⋮ A Nonintrusive Reduced-Order Model for Uncertainty Quantification in Numerical Solution of One-Dimensional Free-Surface Water Flows Over Stochastic Beds ⋮ An optimisation-based domain-decomposition reduced order model for the incompressible Navier-Stokes equations ⋮ Explicit synchronous partitioned scheme for coupled reduced order models based on composite reduced bases ⋮ Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders ⋮ Non-intrusive framework of reduced-order modeling based on proper orthogonal decomposition and polynomial chaos expansion ⋮ Data-driven model order reduction for problems with parameter-dependent jump-discontinuities ⋮ Entropies and Symmetrization of Hyperbolic Stochastic Galerkin Formulations ⋮ Deep learning observables in computational fluid dynamics

Uses Software

• rbMIT

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