[skip tests] [skip docs] start joss paper draft

.github/workflows/JOSSPaper.yml

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+on: [push]
+
+jobs:
+  paper:
+    runs-on: ubuntu-latest
+    name: Paper Draft
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v3
+      - name: Build draft PDF
+        uses: openjournals/openjournals-draft-action@master
+        with:
+          journal: joss
+          # This should be the path to the paper within your repo.
+          paper-path: joss/paper.md
+      - name: Upload
+        uses: actions/upload-artifact@v1
+        with:
+          name: paper
+          # This is the output path where Pandoc will write the compiled
+          # PDF. Note, this should be the same directory as the input
+          # paper.md
+          path: joss/paper.pdf

joss/paper.bib

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+@inproceedings{10.1145/3458817.3476165,
+  author    = {Moses, William S. and Churavy, Valentin and Paehler, Ludger and H\"{u}ckelheim, Jan and Narayanan, Sri Hari Krishna and Schanen, Michel and Doerfert, Johannes},
+  title     = {Reverse-Mode Automatic Differentiation and Optimization of GPU Kernels via Enzyme},
+  year      = {2021},
+  isbn      = {9781450384421},
+  publisher = {Association for Computing Machinery},
+  address   = {New York, NY, USA},
+  url       = {https://doi.org/10.1145/3458817.3476165},
+  doi       = {10.1145/3458817.3476165},
+  booktitle = {Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis},
+  articleno = {61},
+  numpages  = {16},
+  keywords  = {CUDA, LLVM, ROCm, HPC, AD, GPU, automatic differentiation},
+  location  = {St. Louis, Missouri},
+  series    = {SC '21}
+}
+
+@inproceedings{NEURIPS2020_9332c513,
+  author    = {Moses, William and Churavy, Valentin},
+  booktitle = {Advances in Neural Information Processing Systems},
+  editor    = {H. Larochelle and M. Ranzato and R. Hadsell and M. F. Balcan and H. Lin},
+  pages     = {12472--12485},
+  publisher = {Curran Associates, Inc.},
+  title     = {Instead of Rewriting Foreign Code for Machine Learning, Automatically Synthesize Fast Gradients},
+  url       = {https://proceedings.neurips.cc/paper/2020/file/9332c513ef44b682e9347822c2e457ac-Paper.pdf},
+  volume    = {33},
+  year      = {2020}
+}

joss/paper.md

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+---
+title: 'Lux.jl: Bridging Scientific Computing & Deep Learning'
+tags:
+  - Julia
+  - Deep Learning
+  - Scientific Computing
+  - Neural Ordinary Differential Equations
+  - Deep Equilibrium Models
+authors:
+  - name: Avik Pal
+    orcid: 0000-0002-3938-7375
+    affiliation: "1"
+affiliations:
+ - name: Electrical Engineering and Computer Science, CSAIL, MIT
+   index: 1
+date: 2 October 2023
+bibliography: paper.bib
+---
+
+# Summary
+
+Combining Machine Learning and Scientific Computing have recently led to development of
+methods like Universal Differential Equations, Neural Differential Equations, Deep Equilibrium Models, etc.,
+which have been pushing the boundaries of physical sciences. However, every major deep learning
+framework requires the numerical softwares to be rewritten to satisfy their specific requirements.
+Lux.jl is a deep learning framework written in Julia with the correct abstractions to provide seamless
+composability with scientific computing softwares. Lux uses pure functions to provide a
+compiler and automatic differentiation friendly interface without compromising on the performance.
+
+# Statement of Need
+
+## Switching Automatic Differentiation Frameworks
+
+## Support for CPU, NVIDIA GPUs and AMD GPUs
+
+## Composition with Scientific Computing Softwares
+
+## Ecosystem
+
+# Limitations
+
+Lux.jl is still in its early days of development and has the following known limitations:
+
+* Training Small Neural Networks on CPUs are not optimized yet. For small networks,
+  [SimpleChains.jl](https://github.com/PumasAI/SimpleChains.jl) is the fastest option!
+* Nested Automatic Differentiation is current not well supported. We hope to fix this soon,
+  with a migration to Enzyme Automatic Differentiation Framework `[@0.1145/3458817.3476165; @NEURIPS2020_9332c513]`.
+
+# References