Learning physics-based reduced-order models from data using nonlinear manifolds (Q6552123)

scientific article; zbMATH DE number 7861836

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English	Learning physics-based reduced-order models from data using nonlinear manifolds	scientific article; zbMATH DE number 7861836

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Learning physics-based reduced-order models from data using nonlinear manifolds (English)

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8 June 2024

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Publication

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cites work

Learning physics-based models from data: perspectives from inverse problems and model reduction

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Turbulence and the dynamics of coherent structures. I. Coherent structures

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Dynamic mode decomposition of numerical and experimental data

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Dynamic Mode Decomposition and Its Variants

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MODEL REDUCTION FOR FLUIDS, USING BALANCED PROPER ORTHOGONAL DECOMPOSITION

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Reduced Basis Methods for Partial Differential Equations

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Certified real‐time solution of the parametrized steady incompressible Navier–Stokes equations: rigorous reduced‐basis <i>a posteriori</i> error bounds

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Nonlinear Model Reduction via Discrete Empirical Interpolation

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Data-driven operator inference for nonintrusive projection-based model reduction

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Lift \& learn: physics-informed machine learning for large-scale nonlinear dynamical systems

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Über die beste Annäherung von Funktionen einer gegebenen Funktionenklasse

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Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders

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Data-Driven Discovery of Closure Models

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A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder

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POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition

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Quadratic approximation manifold for mitigating the Kolmogorov barrier in nonlinear projection-based model order reduction

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Operator inference for non-intrusive model reduction with quadratic manifolds

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Coupling of substructures for dynamic analyses.

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Eigenmode analysis in unsteady aerodynamics - Reduced-order models

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Spectral analysis of nonlinear flows

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Analysis of Fluid Flows via Spectral Properties of the Koopman Operator

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Approximate inertial manifolds for the Kuramoto-Sivashinsky equation: Analysis and computations

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Two-dimensional invariant manifolds and global bifurcations: Some approximation and visualization studies

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Integral manifolds and inertial manifolds for dissipative partial differential equations

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Some global dynamical properties of a class of pattern formation equations

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Inertial Manifolds for Reaction Diffusion Equations in Higher Space Dimensions

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Low-dimensional models for complex geometry flows: Application to grooved channels and circular cylinders

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The direct extension of ADMM for multi-block convex minimization problems is not necessarily convergent

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A generalized solution of the orthogonal Procrustes problem

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A reduced order method for Allen-Cahn equations

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Two-Sided Projection Methods for Nonlinear Model Order Reduction

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A hierarchy of low-dimensional models for the transient and post-transient cylinder wake

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9 From the POD-Galerkin method to sparse manifold models

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Hamiltonian operator inference: physics-preserving learning of reduced-order models for canonical Hamiltonian systems

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Operator inference with roll outs for learning reduced models from scarce and low-quality data

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Automated solution of differential equations by the finite element method. The FEniCS book

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Identifiers

zbMATH Open document ID

1545.3707

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DOI

10.1063/5.0170105

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Mathematics Subject Classification ID

37M21

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zbMATH DE Number

7861836

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Mathematics(1 entry)

mardi Publication:6552123