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Machine learning design of volume of fluid schemes for compressible flows - MaRDI portal

Machine learning design of volume of fluid schemes for compressible flows (Q2123342)

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Language	Label	Description	Also known as
English	Machine learning design of volume of fluid schemes for compressible flows	scientific article

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scholarly article

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Machine learning design of volume of fluid schemes for compressible flows (English)

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Hervé Jourdren

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Journal of Computational Physics

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publication date

8 April 2022

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zbMATH Keywords

VOF

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CFD

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ML

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describes a project that uses

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MaRDI profile type

MaRDI publication profile

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full work available at URL

https://doi.org/10.1016/j.jcp.2020.109275

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Multi-material interface reconstruction on generalized polyhedral meshes

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Extended ALE method for fluid-structure interaction problems with large structural displacements

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Computational methods in Lagrangian and Eulerian hydrocodes

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A Loosely Coupled Scheme for Fictitious Domain Approximations of Fluid-Structure Interaction Problems with Immersed Thin-Walled Structures

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A Priori Error Estimates for Numerical Methods for Scalar Conservation Laws Part III: Multidimensional Flux-Splitting Monotone Schemes on Non-Cartesian Grids

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Approximation by superpositions of a sigmoidal function

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Nonlinear approximation and (deep) ReLU networks

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Probabilistic analysis of the upwind scheme for transport equations

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An Explicit A Priori Estimate for a Finite Volume Approximation of Linear Advection on Non-Cartesian Grids

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Numerical Methods for Eulerian and Lagrangian Conservation Laws

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An all-Mach method for the simulation of bubble dynamics problems in the presence of surface tension

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Sharp interface approaches and deep learning techniques for multiphase flows

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Numerical approximation of hyperbolic systems of conservation laws

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Numerical resolution of a two-component compressible fluid model with interfaces

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$L^\infty$- and $L^2$-Error Estimates for a Finite Volume Approximation of Linear Advection

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Second-order accurate volume-of-fluid algorithms for tracking material interfaces

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Hidden physics models: machine learning of nonlinear partial differential equations

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An artificial neural network as a troubled-cell indicator

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Reconstructing volume tracking

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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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Identifiers

10.1016/j.jcp.2020.109275

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Mathematics Subject Classification ID

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zbMATH DE Number

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Sitelinks

Mathematics(1 entry)

mardi Publication:2123342

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