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 +1,237 @@
+@software{Abadi_TensorFlow_Large-scale_machine_2015,
+  author = {Abadi, Martín and Agarwal, Ashish and Barham, Paul and Brevdo, Eugene and Chen, Zhifeng and Citro, Craig and Corrado, Greg S. and Davis, Andy and Dean, Jeffrey and Devin, Matthieu and Ghemawat, Sanjay and Goodfellow, Ian and Harp, Andrew and Irving, Geoffrey and Isard, Michael and Jozefowicz, Rafal and Jia, Yangqing and Kaiser, Lukasz and Kudlur, Manjunath and Levenberg, Josh and Mané, Dan and Schuster, Mike and Monga, Rajat and Moore, Sherry and Murray, Derek and Olah, Chris and Shlens, Jonathon and Steiner, Benoit and Sutskever, Ilya and Talwar, Kunal and Tucker, Paul and Vanhoucke, Vincent and Vasudevan, Vijay and Viégas, Fernanda and Vinyals, Oriol and Warden, Pete and Wattenberg, Martin and Wicke, Martin and Yu, Yuan and Zheng, Xiaoqiang},
+  doi = {10.5281/zenodo.4724125},
+  license = {Apache-2.0},
+  month = nov,
+  title = {{TensorFlow, Large-scale machine learning on heterogeneous systems}},
+  year = {2015}
+}
+
+@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{fiats,
+  accessed = {2024-11-13},
+  author   = {Rouson, Damien and Rasmussen, Katherine},
+  title    = {Fiats: Functional inference and training for surrogates},
+  url      = {https://github.com/BerkeleyLab/fiats},
+  year={2024},
+}
+
+@Online{fortran-tf-lib,
+  accessed = {2025-01-30},
+  author   = {Cambridge-ICCS},
+  title    = {fortran-tf-lib},
+  url      = {https://github.com/Cambridge-ICCS/fortran-tf-lib},
+  year={2023},
+}
+
+@Online{fypp,
+  accessed = {2024-11-13},
+  author   = {Aradi, Bálint},
+  title    = {fypp},
+  url      = {https://fypp.readthedocs.io},
+  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}
+}
+
+@article{rasp2018deep,
+  title={Deep learning to represent subgrid processes in climate models},
+  author={Rasp, Stephan and Pritchard, Michael S and Gentine, Pierre},
+  journal={Proceedings of the national academy of sciences},
+  volume={115},
+  number={39},
+  pages={9684--9689},
+  year={2018},
+  publisher={National Academy of Sciences},
+  doi={10.1073/pnas.1810286115}
+}
+
+@article{bony2015clouds,
+  title={Clouds, circulation and climate sensitivity},
+  author={Bony, Sandrine and Stevens, Bjorn and Frierson, Dargan MW and Jakob, Christian and Kageyama, Masa and Pincus, Robert and Shepherd, Theodore G and Sherwood, Steven C and Siebesma, A Pier and Sobel, Adam H and others},
+  journal={Nature Geoscience},
+  volume={8},
+  number={4},
+  pages={261--268},
+  year={2015},
+  publisher={Nature Publishing Group UK London},
+  doi={10.1038/ngeo2398}
+}
+
+@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},
+}
+
+@Online{ICON,
+  accessed = {2025-02-21},
+  author   = {DKRZ},
+  title    = {ICON (Icosahedral Nonhydrostatic) Model},
+  url      = {https://www.icon-model.org/},
+  year={2025},
+}
+
+@Online{CESM,
+  accessed = {2025-02-21},
+  author   = {NCAR},
+  title    = {CESM, the Community Earth System Model},
+  url      = {https://www.cesm.ucar.edu/},
+  year={2025},
+}
+
+
+@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}
+}