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A data-driven surrogate modeling approach for time-dependent incompressible Navier-Stokes equations with dynamic mode decomposition and manifold interpolation - MaRDI portal

A data-driven surrogate modeling approach for time-dependent incompressible Navier-Stokes equations with dynamic mode decomposition and manifold interpolation (Q6038828)

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scientific article; zbMATH DE number 7681467

Language	Label	Description	Also known as
English	A data-driven surrogate modeling approach for time-dependent incompressible Navier-Stokes equations with dynamic mode decomposition and manifold interpolation	scientific article; zbMATH DE number 7681467

Statements

scholarly article

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A data-driven surrogate modeling approach for time-dependent incompressible Navier-Stokes equations with dynamic mode decomposition and manifold interpolation (English)

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Annalisa Quaini

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Gianluigi Rozza

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Advances in Computational Mathematics

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publication date

3 May 2023

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full work available at URL

https://arxiv.org/abs/2201.10872

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zbMATH Keywords

spectral element method

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computational fluid dynamics

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model order reduction

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dynamic mode decomposition

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manifold interpolation

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MaRDI profile type

MaRDI publication profile

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System- and Data-Driven Methods and Algorithms

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Snapshot-Based Methods and Algorithms

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On making large nonlinear problems small

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Multiple-parameter reduced basis technique for bifurcation and post-buckling analyses of composite plates

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Recent advances in reduction methods for instability analysis of structures

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RB (reduced basis) for RB (Rayleigh-Bénard)

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A flexible symmetry-preserving Galerkin/POD reduced order model applied to a convective instability problem

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On the application of reduced basis methods to bifurcation problems in incompressible fluid dynamics

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Reduced basis approaches for parametrized bifurcation problems held by non-linear von Kármán equations

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Driving bifurcating parametrized nonlinear PDEs by optimal control strategies: application to Navier–Stokes equations with model order reduction

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A Reduced Order Modeling Technique to Study Bifurcating Phenomena: Application to the Gross--Pitaevskii Equation

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Compressive Sensing and Low-Rank Libraries for Classification of Bifurcation Regimes in Nonlinear Dynamical Systems

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Sparse Sensing and DMD-Based Identification of Flow Regimes and Bifurcations in Complex Flows

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A comparison of reduced-order modeling approaches using artificial neural networks for PDEs with bifurcating solutions

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A localized reduced-order modeling approach for PDEs with bifurcating solutions

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Non-intrusive reduced order modeling of nonlinear problems using neural networks

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Discovering governing equations from data by sparse identification of nonlinear dynamical systems

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Hamiltonian Systems and Transformation in Hilbert Space

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Dynamic Mode Decomposition with Control

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Dynamic mode decomposition of numerical and experimental data

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A reduced order method for nonlinear parameterized partial differential equations using dynamic mode decomposition coupled with \(k\)-nearest-neighbors regression

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7 Manifold interpolation

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Stability of multiple steady states of convection in laterally heated cavities

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Velocity-Correction Projection Methods for Incompressible Flows

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High-order splitting methods for the incompressible Navier-Stokes equations

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Certified Reduced Basis Methods for Parametrized Partial Differential Equations

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Reduced order methods for modeling and computational reduction

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Reduced-Order Modeling and ROM-Based Optimization of Batch Chromatography

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Higher Order Dynamic Mode Decomposition

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Ergodic Theory, Dynamic Mode Decomposition, and Computation of Spectral Properties of the Koopman Operator

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An Online Method for Interpolating Linear Parametric Reduced-Order Models

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A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems

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Interpolation among reduced‐order matrices to obtain parameterized models for design, optimization and probabilistic analysis

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5 Computational bottlenecks for PROMs: precomputation and hyperreduction

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9 From the POD-Galerkin method to sparse manifold models

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A Locally Parametrized Reduced-Order Model for the Linear Frequency Domain Approach to Time-Accurate Computational Fluid Dynamics

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Data-driven surrogates for high dimensional models using Gaussian process regression on the Grassmann manifold

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Identifiers

zbMATH Open document ID

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Mathematics Subject Classification ID

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zbMATH DE Number

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10.1007/S10444-023-10016-4

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Sitelinks

Mathematics(1 entry)

mardi Publication:6038828

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