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‎.JuliaFormatter.toml

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# Use SciML style: https://github.com/SciML/SciMLStyle
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style = "sciml"
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# Python style alignment. See https://github.com/domluna/JuliaFormatter.jl/pull/732.
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yas_style_nesting = true
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# Align struct fields for better readability of large struct definitions
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align_struct_field = true

‎.github/dependabot.yml

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# https://docs.github.com/github/administering-a-repository/configuration-options-for-dependency-updates
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version: 2
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updates:
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- package-ecosystem: "github-actions"
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directory: "/" # Location of package manifests
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schedule:
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interval: "monthly"

‎.github/workflows/FormatCheck.yml

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name: format-check
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on:
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push:
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branches:
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- 'main'
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tags: '*'
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pull_request:
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jobs:
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check-format:
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runs-on: ${{ matrix.os }}
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strategy:
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matrix:
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julia-version: [1]
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julia-arch: [x86]
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os: [ubuntu-latest]
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steps:
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- uses: julia-actions/setup-julia@latest
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with:
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version: ${{ matrix.julia-version }}
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- uses: actions/checkout@v4
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- name: Install JuliaFormatter and format
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# This will use the latest version by default but you can set the version like so:
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#
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# julia -e 'using Pkg; Pkg.add(PackageSpec(name = "JuliaFormatter", version = "0.13.0"))'
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#
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# TODO: Change the call below to
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# format(".")
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run: |
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julia -e 'using Pkg; Pkg.add(PackageSpec(name = "JuliaFormatter"))'
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julia -e 'using JuliaFormatter; format(["examples", "src", "test"])'
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- name: Format check
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run: |
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julia -e '
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out = Cmd(`git diff --name-only`) |> read |> String
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if out == ""
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exit(0)
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else
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@error "Some files have not been formatted !!!"
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write(stdout, out)
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exit(1)
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end'

‎.github/workflows/SpellCheck.yml

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name: Spell Check
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on: [pull_request, workflow_dispatch]
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jobs:
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typos-check:
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name: Spell Check with Typos
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runs-on: ubuntu-latest
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steps:
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- name: Checkout Actions Repository
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uses: actions/checkout@v4
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- name: Check spelling
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uses: crate-ci/[email protected]

‎.github/workflows/ci.yml

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name: CI
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on:
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push:
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branches:
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- main
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paths-ignore:
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- 'AUTHORS.md'
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- 'LICENSE.md'
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- 'README.md'
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pull_request:
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paths-ignore:
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- 'AUTHORS.md'
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- 'LICENSE.md'
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- 'README.md'
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workflow_dispatch:
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# Cancel redundant CI tests automatically
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concurrency:
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group: ${{ github.workflow }}-${{ github.ref }}
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cancel-in-progress: true
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jobs:
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test:
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if: "!contains(github.event.head_commit.message, 'skip ci')"
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# We could also include the Julia version as in
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# name: ${{ matrix.trixi_test }} - ${{ matrix.os }} - Julia ${{ matrix.version }} - ${{ matrix.arch }} - ${{ github.event_name }}
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# to be more specific. However, that requires us updating the required CI tests whenever we update Julia.
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name: ${{ matrix.trixi_test }} - ${{ matrix.os }} - ${{ matrix.arch }} - ${{ github.event_name }}
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runs-on: ${{ matrix.os }}
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strategy:
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fail-fast: false
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matrix:
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version:
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- '1.9'
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os:
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- ubuntu-latest
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arch:
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- x64
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steps:
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- uses: actions/checkout@v4
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- uses: julia-actions/setup-julia@v1
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with:
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version: ${{ matrix.version }}
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arch: ${{ matrix.arch }}
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- run: julia -e 'using InteractiveUtils; versioninfo(verbose=true)'
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- uses: julia-actions/cache@v1
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- uses: julia-actions/julia-buildpkg@v1
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env:
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PYTHON: ""
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- name: Run tests with coverage
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uses: julia-actions/julia-runtest@v1
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with:
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coverage: true
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- uses: julia-actions/julia-processcoverage@v1
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with:
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directories: src,examples
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- uses: coverallsapp/github-action@master
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with:
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github-token: ${{ secrets.GITHUB_TOKEN }}
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flag-name: run-${{ matrix.trixi_test }}-${{ matrix.os }}-${{ matrix.version }}-${{ matrix.arch }}-${{ github.run_id }}
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parallel: false
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path-to-lcov: ./lcov.info

‎.gitignore

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*.pdf
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*.png
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*.h5
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*.mp4
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*.mem
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*.vtu
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*.pvd
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*.avi
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*.ogv
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*.mesh
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*.bson
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*.inp
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**/Manifest.toml
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out*/
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docs/build
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docs/src/notebooks
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docs/src/authors.md
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docs/src/tutorials
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public/
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coverage/
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coverage_report/
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**/*.jl.*.cov
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**/.ipynb_checkpoints/
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**/solution_*.txt
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**/restart_*.txt
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.vscode/
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.DS_Store
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run
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run/*
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LocalPreferences.toml

‎.typos.toml

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[default.extend-words]
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rcall = "rcall"
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claus = "claus"
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dum = "dum"

‎LICENSE.md

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MIT License
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Copyright (c) 2023-present Michael Schlottke-Lakemper, Julia Odenthal
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.

‎Project.toml

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name = "TrixiSmartShockFinder"
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uuid = "6b42f25e-8cc0-41c2-936f-439385811e39"
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authors = ["Michael Schlottke-Lakemper <[email protected]> and contributors"]
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version = "0.1.0"
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[deps]
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Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
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MuladdMacro = "46d2c3a1-f734-5fdb-9937-b9b9aeba4221"
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Trixi = "a7f1ee26-1774-49b1-8366-f1abc58fbfcb"
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[compat]
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Flux = "0.13.15, 0.14"
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MuladdMacro = "0.2.4"
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# Trixi = "0.6"
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julia = "1.9"

‎README.md

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# TrixiSmartShockFinder.jl
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Spin-off repository of Trixi.jl with neural network-based shock indicators
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[![Build Status](https://github.com/trixi-framework/TrixiSmartShockFinder.jl/workflows/CI/badge.svg)](https://github.com/trixi-framework/TrixiSmartShockFinder.jl/actions?query=workflow%3ACI)
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[![Coveralls](https://coveralls.io/repos/github/trixi-framework/TrixiSmartShockFinder.jl/badge.svg?branch=main)](https://coveralls.io/github/trixi-framework/TrixiSmartShockFinder.jl?branch=main)
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[![License: MIT](https://img.shields.io/badge/License-MIT-success.svg)](https://opensource.org/licenses/MIT)
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Spin-off repository of [Trixi.jl](https://github.com/trixi-framework/Trixi.jl)
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with neural network-based shock indicators.
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**Note: This repository is currently not under development and has been archived. If you are
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interested in using any of this, please get in touch with the developers of the
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[Trixi.jl](https://github.com/trixi-framework/Trixi.jl) package.
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## Usage
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To run any of the elixirs with the neurl network-based indicators, you first need to install
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all required auxiliary packages by running the following code in the Julia REPL:
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```julia
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julia> using Pkg
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julia> Pkg.add(["BSON", "Flux", "OrdinaryDiffEq", "Trixi"])
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```
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Then, clone this repository
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```shell
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git clone [email protected]:trixi-framework/TrixiSmartShockFinder.jl.git
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```
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enter the directory, and start Julia (tested with Julia v1.9) with the project set to
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the clone directory
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```
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cd TrixiSmartShockFinder.jl
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julia --project=.
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```
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Now run one of the elixirs in the `examples` folder, e.g.,
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```julia
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using TrixiSmartShockFinder
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trixi_include("examples/tree_2d_dgsem/elixir_euler_blast_wave_neuralnetwork_perssonperaire.jl")
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```
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## Authors
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TrixiSmartShockFinder.jl was initiated by
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[Michael Schlottke-Lakemper](https://lakemper.eu) (University of Augsburg, Germany) and
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Julia Odenthal (University of Cologne, Germany).
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## License and contributing
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TrixiSmartShockFinder.jl is licensed under the MIT license (see [LICENSE.md](LICENSE.md)).

‎examples/tree_1d_dgsem/elixir_euler_blast_wave_neuralnetwork_perssonperaire.jl

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using Downloads: download
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using Flux
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using BSON: load
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network = joinpath(@__DIR__, "modelnnpp-0.97-0.0001.bson")
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download("https://github.com/trixi-framework/Trixi_IndicatorNeuralNetwork_networks/raw/main/networks/modelnnpp-0.97-0.0001.bson",
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network)
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model1d = load(network, @__MODULE__)[:model1d]
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using OrdinaryDiffEq
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using TrixiSmartShockFinder
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using Trixi
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# TODO: Remove once Trixi.jl is released without these exports
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NeuralNetworkPerssonPeraire = TrixiSmartShockFinder.NeuralNetworkPerssonPeraire
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NeuralNetworkRayHesthaven = TrixiSmartShockFinder.NeuralNetworkRayHesthaven
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IndicatorNeuralNetwork = TrixiSmartShockFinder.IndicatorNeuralNetwork
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# This elixir was one of the setups used in the following master thesis:
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# - Julia Odenthal (2021)
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# Shock capturing with artificial neural networks
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# University of Cologne, advisors: Gregor Gassner, Michael Schlottke-Lakemper
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# This motivates the particular choice of fluxes, mesh resolution etc.
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###############################################################################
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# semidiscretization of the compressible Euler equations
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equations = CompressibleEulerEquations1D(1.4)
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"""
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initial_condition_blast_wave(x, t, equations::CompressibleEulerEquations1D)
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A medium blast wave taken from
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- Sebastian Hennemann, Gregor J. Gassner (2020)
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A provably entropy stable subcell shock capturing approach for high order split form DG
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[arXiv: 2008.12044](https://arxiv.org/abs/2008.12044)
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"""
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function initial_condition_blast_wave(x, t, equations::CompressibleEulerEquations1D)
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# Modified From Hennemann & Gassner JCP paper 2020 (Sec. 6.3) -> "medium blast wave"
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# Set up polar coordinates
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inicenter = SVector(0.0)
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x_norm = x[1] - inicenter[1]
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r = abs(x_norm)
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# The following code is equivalent to
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# phi = atan(0.0, x_norm)
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# cos_phi = cos(phi)
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# in 1D but faster
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cos_phi = x_norm > 0 ? one(x_norm) : -one(x_norm)
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# Calculate primitive variables
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rho = r > 0.5 ? 1.0 : 1.1691
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v1 = r > 0.5 ? 0.0 : 0.1882 * cos_phi
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p = r > 0.5 ? 1.0E-3 : 1.245
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return prim2cons(SVector(rho, v1, p), equations)
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end
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initial_condition = initial_condition_blast_wave
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surface_flux = flux_lax_friedrichs
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volume_flux = flux_chandrashekar
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basis = LobattoLegendreBasis(3)
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indicator_sc = IndicatorNeuralNetwork(equations, basis,
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indicator_type = NeuralNetworkPerssonPeraire(),
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alpha_max = 0.5,
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alpha_min = 0.001,
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alpha_smooth = true,
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alpha_continuous = false,
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alpha_amr = false,
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variable = density_pressure,
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network = model1d)
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volume_integral = VolumeIntegralShockCapturingHG(indicator_sc;
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volume_flux_dg = volume_flux,
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volume_flux_fv = surface_flux)
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solver = DGSEM(basis, surface_flux, volume_integral)
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coordinates_min = (-2.0,)
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coordinates_max = (2.0,)
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mesh = TreeMesh(coordinates_min, coordinates_max,
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initial_refinement_level = 6,
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n_cells_max = 10_000)
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semi = SemidiscretizationHyperbolic(mesh, equations, initial_condition, solver)
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###############################################################################
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# ODE solvers, callbacks etc.
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tspan = (0.0, 12.5)
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ode = semidiscretize(semi, tspan)
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summary_callback = SummaryCallback()
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analysis_interval = 100
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analysis_callback = AnalysisCallback(semi, interval = analysis_interval)
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alive_callback = AliveCallback(analysis_interval = analysis_interval)
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save_solution = SaveSolutionCallback(interval = 100,
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save_initial_solution = true,
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save_final_solution = true,
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solution_variables = cons2prim)
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stepsize_callback = StepsizeCallback(cfl = 0.5)
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callbacks = CallbackSet(summary_callback,
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analysis_callback, alive_callback,
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save_solution,
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stepsize_callback)
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###############################################################################
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# run the simulation
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sol = solve(ode, CarpenterKennedy2N54(williamson_condition = false),
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dt = 1.0, # solve needs some value here but it will be overwritten by the stepsize_callback
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save_everystep = false, callback = callbacks);
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summary_callback() # print the timer summary

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