Physics-informed machine learning for reduced-order modeling of nonlinear problems (Q2133556)

scientific article

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

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instance of

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title

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

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DOI

10.1016/j.jcp.2021.110666

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Journal of Computational Physics

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publication date

29 April 2022

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Mathematics Subject Classification ID

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physics-informed machine learning

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feedforward neural network

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reduced-order modeling

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nonlinear PDE

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describes a project that uses

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MaRDI publication profile

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full work available at URL

https://doi.org/10.1016/j.jcp.2021.110666

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OpenAlex ID

W3137611682

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cites work

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On the stability of the reduced basis method for Stokes equations in parametrized domains

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Online Adaptive Model Reduction for Nonlinear Systems via Low-Rank Updates

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

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Transport, Collective Motion, and Brownian Motion

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A dynamic subgrid scale model for large eddy simulations based on the Mori-Zwanzig formalism

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

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Non-intrusive reduced order modeling of unsteady flows using artificial neural networks with application to a combustion problem

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Mathematics(1 entry)

mardi Publication:2133556