Non-intrusive reduced-order modeling for uncertainty quantification of space-time-dependent parameterized problems

DOI10.1016/j.camwa.2021.01.015OpenAlexW3133582528MaRDI QIDQ2656003

Xiang Sun, Jung-il Choi

Publication date: 18 March 2021

Published in: Computers \& Mathematics with Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.camwa.2021.01.015

zbMATH Keywords

proper orthogonal decompositionuncertainty quantificationreduced-order modelingpolynomial chaos expansion

Mathematics Subject Classification ID

Navier-Stokes equations for incompressible viscous fluids (76D05) Dependence of solutions to PDEs on initial and/or boundary data and/or on parameters of PDEs (35B30) Finite element methods applied to problems in fluid mechanics (76M10) Finite element, Rayleigh-Ritz and Galerkin methods for initial value and initial-boundary value problems involving PDEs (65M60) Numerical methods for partial differential equations, boundary value problems (65N99)

Related Items

A reduced order method for nonlinear parameterized partial differential equations using dynamic mode decomposition coupled with \(k\)-nearest-neighbors regression ⋮ A dynamic mode decomposition based reduced-order model for parameterized time-dependent partial differential equations ⋮ A non-intrusive reduced-order modeling for uncertainty propagation of time-dependent problems using a B-splines Bézier elements-based method and proper orthogonal decomposition: application to dam-break flows ⋮ An enhanced algorithm for online proper orthogonal decomposition and its parallelization for unsteady simulations

Uses Software

• Matlab
• redbKIT

Cites Work

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