JOSS paper submission

.github/workflows/JOSS_paper_pdf.yml

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+# Workflow to render the FTorch submission to JOSS
+name: RenderJOSSPaper
+
+# Controls when the workflow will run
+on:
+  # Triggers the workflow on pushes to the "main" branch, i.e., PR merges
+  push:
+    branches: [ "main" ]
+
+  # Triggers the workflow on pushes to open pull requests to main with documentation changes
+  pull_request:
+    branches: [ "main" ]
+    paths:
+      - '.github/workflows/JOSS_paper_pdf.yml'
+      - 'paper/*'
+
+jobs:
+  paper:
+    runs-on: ubuntu-latest
+    name: Paper Draft
+    steps:
+      # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it
+      - name: Checkout
+        uses: actions/checkout@v4
+        with:
+          persist-credentials: false
+
+      # Builds/renders the paper using the openjournals action
+      - name: Build draft PDF
+        uses: openjournals/openjournals-draft-action@master
+        with:
+          journal: joss
+          paper-path: paper/paper.md
+
+      # Uploads the rendered pdf to GitHub.
+      - name: Upload draft PDF
+        uses: actions/upload-artifact@v4
+        with:
+          name: paper
+          path: paper/paper.pdf

paper/paper.bib

@@ -0,0 +1,210 @@
+@article{bishara2023state,
+  title={A state-of-the-art review on machine learning-based multiscale modeling, simulation, homogenization and design of materials},
+  author={Bishara, Dana and Xie, Yuxi and Liu, Wing Kam and Li, Shaofan},
+  journal={Archives of computational methods in engineering},
+  volume={30},
+  number={1},
+  pages={191--222},
+  year={2023},
+  publisher={Springer},
+  doi={10.1007/s11831-022-09795-8}
+}
+
+@article{espinosa2022machine,
+  title={Machine learning gravity wave parameterization generalizes to capture the QBO and response to increased CO2},
+  author={Espinosa, Zachary I and Sheshadri, Aditi and Cain, Gerald R and Gerber, Edwin P and DallaSanta, Kevin J},
+  journal={Geophysical Research Letters},
+  volume={49},
+  number={8},
+  pages={e2022GL098174},
+  year={2022},
+  publisher={Wiley Online Library},
+  doi={10.1029/2022GL098174}
+}
+
+@Online{fortitude,
+  accessed = {2024-11-13},
+  author   = {Pattinson, Liam and Hill, Peter},
+  title    = {Fortitude},
+  url      = {https://github.com/PlasmaFAIR/fortitude},
+  year={2024},
+}
+
+@Online{fypp,
+  accessed = {2024-11-13},
+  author   = {Aradi, Bálint},
+  title    = {fypp},
+  url      = {https://fypp.readthedocs.io},
+  year={2024},
+}
+
+@Online{clangformat,
+  accessed = {2024-11-13},
+  author   = {{The Clang Team}},
+  title    = {clang-format},
+  url      = {https://clang.llvm.org/docs/ClangFormat.html},
+  year={2024},
+}
+
+@Online{clangtidy,
+  accessed = {2024-11-13},
+  author   = {{The Clang Team}},
+  title    = {clang-tidy},
+  url      = {https://clang.llvm.org/extra/clang-tidy/},
+  year={2024},
+}
+
+@Online{ruff,
+  accessed = {2024-11-13},
+  author   = {Astral},
+  title    = {ruff},
+  url      = {https://docs.astral.sh/ruff/},
+  year={2024},
+}
+
+@article{kashinath2021physics,
+  title={Physics-informed machine learning: case studies for weather and climate modelling},
+  author={Kashinath, Karthik and Mustafa, M and Albert, Adrian and Wu, JL and Jiang, C and Esmaeilzadeh, Soheil and Azizzadenesheli, Kamyar and Wang, R and Chattopadhyay, A and Singh, A and others},
+  journal={Philosophical Transactions of the Royal Society A},
+  volume={379},
+  number={2194},
+  pages={20200093},
+  year={2021},
+  publisher={The Royal Society Publishing},
+  doi={10.1098/rsta.2020.0093}
+}
+
+@article{kedward2022state,
+  title={The state of {F}ortran},
+  author={Kedward, Laurence J and Aradi, B{\'a}lint and {\v{C}}ert{\'\i}k, Ond{\v{r}}ej and Curcic, Milan and Ehlert, Sebastian and Engel, Philipp and Goswami, Rohit and Hirsch, Michael and Lozada-Blanco, Asdrubal and Magnin, Vincent and others},
+  journal={Computing in Science \& Engineering},
+  volume={24},
+  number={2},
+  pages={63--72},
+  year={2022},
+  publisher={IEEE},
+  doi={10.1109/MCSE.2022.3159862}
+}
+
+@article{carleo2019machine,
+  title={Machine learning and the physical sciences},
+  author={Carleo, Giuseppe and Cirac, Ignacio and Cranmer, Kyle and Daudet, Laurent and Schuld, Maria and Tishby, Naftali and Vogt-Maranto, Leslie and Zdeborov{\'a}, Lenka},
+  journal={Reviews of Modern Physics},
+  volume={91},
+  number={4},
+  pages={045002},
+  year={2019},
+  publisher={APS},
+  doi={10.1103/RevModPhys.91.045002}
+}
+
+@article{paszke2019pytorch,
+  title={Pytorch: An imperative style, high-performance deep learning library},
+  author={Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and others},
+  journal={Advances in neural information processing systems},
+  volume={32},
+  year={2019}
+}
+
+@Online{MiMAML,
+  accessed = {2023-10-11},
+  author   = {{DataWave}},
+  title    = {MiMA Machine Learning},
+  url      = {https://github.com/DataWaveProject/MiMA-machine-learning},
+  year={2023},
+}
+
+@article{mansfield2024uncertainty,
+  title={Uncertainty quantification of a machine learning subgrid-scale parameterization for atmospheric gravity waves},
+  author={Mansfield, Laura A and Sheshadri, Aditi},
+  journal={Journal of Advances in Modeling Earth Systems},
+  volume={16},
+  number={7},
+  pages={e2024MS004292},
+  year={2024},
+  publisher={Wiley Online Library},
+  doi={10.1029/2024MS004292}
+}
+
+@Online{CAMML,
+  accessed = {2024-03-25},
+  author   = {{M2LInES}},
+  title    = {CAM-ML},
+  url      = {https://github.com/m2lines/CAM-ML},
+}
+
+@Online{CAMGW,
+  accessed = {2024-03-25},
+  author   = {{DataWave}},
+  title    = {DataWave CAM-GW},
+  url      = {https://github.com/DataWaveProject/CAM},
+  year={2024},
+}
+
+@Online{forpy,
+  accessed = {2023-10-11},
+  author   = {Rabel, Elias},
+  title    = {forpy},
+  url      = {https://github.com/ylikx/forpy},
+  year={2020},
+}
+
+@Online{pytorchfortran,
+  accessed = {2024-06-14},
+  author   = {Alexeev, Dmitry},
+  title    = {pytorch-fortran},
+  url      = {https://github.com/alexeedm/pytorch-fortran},
+  year={2024},
+}
+
+@Online{torchfort,
+  accessed = {2024-06-14},
+  author   = {NVIDIA},
+  title    = {TorchFort},
+  url      = {https://nvidia.github.io/TorchFort/},
+  year={2024},
+}
+
+@article{brenowitz2020machine,
+  title={Machine learning climate model dynamics: Offline versus online performance},
+  author={Brenowitz, Noah D and Henn, Brian and McGibbon, Jeremy and Clark, Spencer K and Kwa, Anna and Perkins, W Andre and Watt-Meyer, Oliver and Bretherton, Christopher S},
+  journal={arXiv preprint arXiv:2011.03081},
+  year={2020},
+  doi={10.48550/arXiv.2011.03081}
+}
+
+@article{partee2022using,
+  title={Using machine learning at scale in numerical simulations with SmartSim: An application to ocean climate modeling},
+  author={Partee, Sam and Ellis, Matthew and Rigazzi, Alessandro and Shao, Andrew E and Bachman, Scott and Marques, Gustavo and Robbins, Benjamin},
+  journal={Journal of Computational Science},
+  volume={62},
+  pages={101707},
+  year={2022},
+  publisher={Elsevier},
+  doi = {10.1016/j.jocs.2022.101707},
+  url = {https://www.sciencedirect.com/science/article/pii/S1877750322001065},
+}
+
+@article{heuer2024interpretable,
+  title={Interpretable multiscale machine learning-based parameterizations of convection for ICON},
+  author={Heuer, Helge and Schwabe, Mierk and Gentine, Pierre and Giorgetta, Marco A and Eyring, Veronika},
+  journal={Journal of Advances in Modeling Earth Systems},
+  volume={16},
+  number={8},
+  pages={e2024MS004398},
+  year={2024},
+  publisher={Wiley Online Library},
+  doi={10.1029/2024MS004398}
+}
+
+@inproceedings{curcic2019parallel,
+  title={A parallel {F}ortran framework for neural networks and deep learning},
+  author={Curcic, Milan},
+  booktitle={ACM SIGPLAN Fortran Forum},
+  volume={38},
+  number={1},
+  pages={4--21},
+  year={2019},
+  organization={ACM New York, NY, USA},
+  doi={10.1145/3323057.3323059}
+}