A graph convolutional autoencoder approach to model order reduction for parametrized PDEs (Q6126547)

scientific article; zbMATH DE number 7829497

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English	A graph convolutional autoencoder approach to model order reduction for parametrized PDEs	scientific article; zbMATH DE number 7829497

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instance of

scholarly article

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title

A graph convolutional autoencoder approach to model order reduction for parametrized PDEs (English)

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Journal of Computational Physics

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publication date

9 April 2024

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full work available at URL

https://arxiv.org/abs/2305.08573

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zbMATH Keywords

nonlinear reduced order modeling

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parametric PDEs

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graph neural networks

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convolutional autoencoder

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geometrical parameters

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MaRDI profile type

MaRDI publication profile

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cites work

Model Reduction and Approximation

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Reduced Basis Methods for Partial Differential Equations

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

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A comprehensive deep learning-based approach to reduced order modeling of nonlinear time-dependent parametrized PDEs

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Neural network modeling for near wall turbulent flow.

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Deep learning of thermodynamics-aware reduced-order models from data

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

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An artificial neural network approach to bifurcating phenomena in computational fluid dynamics

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A continuous convolutional trainable filter for modelling unstructured data

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A comparison of neural network architectures for data-driven reduced-order modeling

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An `empirical interpolation' method: Application to efficient reduced-basis discretization of partial differential equations

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Nonlinear Model Reduction via Discrete Empirical Interpolation

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Physics-informed machine learning for reduced-order modeling of nonlinear problems

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Decay of the Kolmogorov \(N\)-width for wave problems

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A Registration Method for Model Order Reduction: Data Compression and Geometry Reduction

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Nonlinear reduced basis approximation of parameterized evolution equations via the method of freezing

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The Shifted Proper Orthogonal Decomposition: A Mode Decomposition for Multiple Transport Phenomena

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Nonlinear model order reduction based on local reduced-order bases

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Nonlinear model reduction on metric spaces. Application to one-dimensional conservative PDEs in Wasserstein spaces

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Non-linear manifold reduced-order models with convolutional autoencoders and reduced over-collocation method

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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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Deep-HyROMnet: a deep learning-based operator approximation for hyper-reduction of nonlinear parametrized PDEs

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Reduced order modeling for nonlinear structural analysis using Gaussian process regression

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

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Algebraic multigrid methods

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Multiscale graph neural network autoencoders for interpretable scientific machine learning

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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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Agglomeration of polygonal grids using graph neural networks with applications to multigrid solvers

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Physics-informed graph neural Galerkin networks: a unified framework for solving PDE-governed forward and inverse problems

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

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Identifiers

arXiv ID

2305.08573

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Mathematics Subject Classification ID

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10.1016/J.JCP.2024.112762

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Sitelinks

Mathematics(1 entry)

mardi Publication:6126547