Latent-space time evolution of non-intrusive reduced-order models using Gaussian process emulation (Q2115690)

scientific article; zbMATH DE number 7493969

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English	Latent-space time evolution of non-intrusive reduced-order models using Gaussian process emulation	scientific article; zbMATH DE number 7493969

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instance of

scholarly article

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title

Latent-space time evolution of non-intrusive reduced-order models using Gaussian process emulation (English)

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21 March 2022

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full work available at URL

https://arxiv.org/abs/2007.12167

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zbMATH Keywords

reduced-order models

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deep learning

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Gaussian process regression

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describes a project that uses

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Direct numerical simulation of turbulence at lower costs

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PDE-Net 2.0: learning PDEs from data with a numeric-symbolic hybrid deep network

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Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations

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Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

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DGM: a deep learning algorithm for solving partial differential equations

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Q2957673

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Neural network closures for nonlinear model order reduction

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

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An intrinsic stabilization scheme for proper orthogonal decomposition based low-dimensional models

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Time-series learning of latent-space dynamics for reduced-order model closure

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On the stability and convergence of a Galerkin reduced order model (ROM) of compressible flow with solid wall and far-field boundary treatment

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Efficient non linear model reduction via a least-squares Petrov-Galerkin projection and compressive tensor approximations

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Non-linear Petrov-Galerkin methods for reduced order modelling of the Navier-Stokes equations using a mixed finite element pair

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Non-linear Petrov-Galerkin methods for reduced order hyperbolic equations and discontinuous finite element methods

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

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Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks

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A long short-term memory embedding for hybrid uplifted reduced order models

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Recurrent neural network closure of parametric POD-Galerkin reduced-order models based on the Mori-Zwanzig formalism

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Multi-level convolutional autoencoder networks for parametric prediction of spatio-temporal dynamics

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Data-driven discovery of coordinates and governing equations

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Proper orthogonal decomposition closure models for turbulent flows: a numerical comparison

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Multiresolution dynamic mode decomposition

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Model reduction for compressible flows using POD and Galerkin projection

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Identifiers

Mathematics Subject Classification ID

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10.1016/J.PHYSD.2020.132797

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Sitelinks

Mathematics(1 entry)

mardi Publication:2115690