A multifidelity deep operator network approach to closure for multiscale systems (Q6116145)

scientific article; zbMATH DE number 7725699

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English	A multifidelity deep operator network approach to closure for multiscale systems	scientific article; zbMATH DE number 7725699

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instance of

scholarly article

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title

A multifidelity deep operator network approach to closure for multiscale systems (English)

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arXiv ID

2303.08893

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DOI

10.1016/j.cma.2023.116161

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Computer Methods in Applied Mechanics and Engineering

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publication date

11 August 2023

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full work available at URL

https://arxiv.org/abs/2303.08893

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Mathematics Subject Classification ID

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reduced order models

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in-the-loop training

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differentiable physics

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multifidelity learning

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MaRDI profile type

MaRDI publication profile

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OpenAlex ID

W4381548105

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cites work

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Stability properties of POD-Galerkin approximations for the compressible Navier-Stokes equations

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The real butterfly effect

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Low-dimensional modelling of high-Reynolds-number shear flows incorporating constraints from the Navier–Stokes equation

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Minimal subspace rotation on the Stiefel manifold for stabilization and enhancement of projection-based reduced order models for the compressible Navier-Stokes equations

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The dynamics of coherent structures in the wall region of a turbulent boundary layer

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A spectral viscosity method for correcting the long-term behavior of POD models.

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Artificial viscosity proper orthogonal decomposition

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Problem reduction, renormalization, and memory

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Data-Driven Discovery of Closure Models

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Stabilized neural ordinary differential equations for long-time forecasting of dynamical systems

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Stable \textit{a posteriori} LES of 2D turbulence using convolutional neural networks: backscattering analysis and generalization to higher \(Re\) via transfer learning

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Frame invariant neural network closures for Kraichnan turbulence

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A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data

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Learned turbulence modelling with differentiable fluid solvers: physics-based loss functions and optimisation horizons

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Improved architectures and training algorithms for deep operator networks

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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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On the stability of projection-based model order reduction for convection-dominated laminar and turbulent flows

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

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Galerkin v. least-squares Petrov-Galerkin projection in nonlinear model reduction

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Windowed least-squares model reduction for dynamical systems

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Windowed space-time least-squares Petrov-Galerkin model order reduction for nonlinear dynamical systems

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Comparative numerical analysis using reduced-order modeling strategies for nonlinear large-scale systems

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The GNAT method for nonlinear model reduction: effective implementation and application to computational fluid dynamics and turbulent flows

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Data-driven variational multiscale reduced order models

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Error estimates for DeepONets: a deep learning framework in infinite dimensions

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Model Reduction for Transport-Dominated Problems via Online Adaptive Bases and Adaptive Sampling

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Real-time reduced-order modeling of stochastic partial differential equations via time-dependent subspaces

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On-the-fly reduced order modeling of passive and reactive species via time-dependent manifolds

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Sitelinks

Mathematics(1 entry)

mardi Publication:6116145