divergenceconsistency

Source code for the paper "Discretize first, filter next: Learning divergence-consistent closure models for large-eddy simulation".

https://github.com/agdestein/divergenceconsistency

Science Score: 54.0%

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CITATION.cff file
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Last synced: 10 months ago · JSON representation ·

Repository

Source code for the paper "Discretize first, filter next: Learning divergence-consistent closure models for large-eddy simulation".

Basic Info

Host: GitHub
Owner: agdestein
Language: TeX
Default Branch: main
Homepage: https://www.sciencedirect.com/science/article/pii/S0021999124008258
Size: 3.35 MB

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Open Issues: 0
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Created over 1 year ago · Last pushed over 1 year ago

Metadata Files

Readme Citation

README.md

DivergenceConsistency

This repository contains source code for the paper "Discretize first, filter next: Learning divergence-consistent closure models for large-eddy simulation".

See PaperDC for how to run the scripts.

See LaTeXFiles for the source files of the paper.

External links

Published version
ArXiv preprint
IncompressibleNavierStokes.jl: Incompressible Navier-Stokes solver used for simulations.

Owner

Name: Syver Døving Agdestein
Login: agdestein
Kind: user
Location: Amsterdam
Company: Centrum Wiskunde & Informatica

Website: https://agdestein.github.io/
Repositories: 4
Profile: https://github.com/agdestein

PhD candidate in scientific computing

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use this work, please cite it using the following information."
url: "https://github.com/agdestein/DivergenceConsistency"
version: "1.0.0"
type: software
title: "PaperDC"
authors:
  - family-names: "Agdestein"
    given-names: "Syver Døving"
    orcid: "https://orcid.org/0000-0002-1589-2916"
  - family-names: "Sanderse"
    given-names: "Benjamin"
    orcid: "https://orcid.org/0000-0001-9483-1988"
preferred-citation:
    type: article
    authors:
      - family-names: "Agdestein"
        given-names: "Syver Døving"
        orcid: "https://orcid.org/0000-0002-1589-2916"
      - family-names: "Sanderse"
        given-names: "Benjamin"
        orcid: "https://orcid.org/0000-0001-9483-1988"
    title: "Discretize first, filter next: Learning divergence-consistent closure models for large-eddy simulation"
    doi: "10.1016/j.jcp.2024.113577"
    date-released: "2024-11-15"
    journal: "Journal of Computational Physics"
    url: "https://www.sciencedirect.com/science/article/pii/S0021999124008258"
    month: 11
    year: 2025
    issn: "0021-9991"
    volume: 522
    pages: 113577
    abstract: |
      We propose a new neural network based large eddy simulation framework for the incompressible Navier-Stokes equations based on the paradigm “discretize first, filter and close next”. This leads to full model-data consistency and allows for employing neural closure models in the same environment as where they have been trained. Since the LES discretization error is included in the learning process, the closure models can learn to account for the discretization.
      Furthermore, we employ a divergence-consistent discrete filter defined through face-averaging and provide novel theoretical and numerical filter analysis. This filter preserves the discrete divergence-free constraint by construction, unlike general discrete filters such as volume-averaging filters. We show that using a divergence-consistent LES formulation coupled with a convolutional neural closure model produces stable and accurate results for both a-priori and a-posteriori training, while a general (divergence-inconsistent) LES model requires a-posteriori training or other stability-enforcing measures.
    keywords:
      - "Discrete filtering"
      - "Closure modeling"
      - "Divergence-consistency"
      - "Large-eddy simulation"
      - "Neural ODE"
      - "A-posteriori training"

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