Paper WIP, now with github action

.github/workflows/draft-pdf.yml

@@ -0,0 +1,24 @@
+name: Draft PDF
+on: [push]
+
+jobs:
+  paper:
+    runs-on: ubuntu-latest
+    name: Paper Draft
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+      - 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: paper/paper.md
+      - name: Upload
+        uses: actions/upload-artifact@v4
+        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: paper/paper.pdf

paper/paper.bib

@@ -1,5 +1,3 @@
-
-
 @InProceedings{krämer2021probabilistic,
   title =        {Probabilistic {ODE} Solutions in Millions of Dimensions},
   author =       {Kr{\"a}mer, Nicholas and Bosch, Nathanael and Schmidt,
@@ -57,7 +55,6 @@ @inproceedings{beck2024diffusion
   url =          {https://openreview.net/forum?id=43HZG9zwaj}
 }
 
-
 @misc{wenger2021probnum,
   title =        {ProbNum: Probabilistic Numerics in Python},
   author =       {Jonathan Wenger and Nicholas Krämer and Marvin Pförtner and
@@ -68,4 +65,50 @@ @misc{wenger2021probnum
   eprint =       {2112.02100},
   archivePrefix ={arXiv},
   primaryClass = {cs.MS}
+}
+
+@InProceedings{dalton2024,
+  title = 	 {Data-Adaptive Probabilistic Likelihood Approximation for Ordinary Differential Equations},
+  author =       {Wu, Mohan and Lysy, Martin},
+  booktitle = 	 {Proceedings of The 27th International Conference on Artificial Intelligence and Statistics},
+  pages = 	 {1018--1026},
+  year = 	 {2024},
+  editor = 	 {Dasgupta, Sanjoy and Mandt, Stephan and Li, Yingzhen},
+  volume = 	 {238},
+  series = 	 {Proceedings of Machine Learning Research},
+  month = 	 {02--04 May},
+  publisher =    {PMLR},
+  pdf = 	 {https://proceedings.mlr.press/v238/wu24b/wu24b.pdf},
+  url = 	 {https://proceedings.mlr.press/v238/wu24b.html},
+}
+
+@article{rackauckas2017differentialequations,
+  title =        {{DifferentialEquations.jl} – A Performant and Feature-Rich
+                  Ecosystem for Solving Differential Equations in Julia},
+  author =       {Rackauckas, Christopher and Nie, Qing},
+  journal =      {Journal of Open Research Software},
+  volume =       5,
+  number =       1,
+  year =         2017,
+  publisher =    {Ubiquity Press}
+}
+
+@misc{probdiffeq,
+  author = {Kr{\"a}mer, Nicholas},
+  title = {probdiffeq: Probabilistic solvers for differential equations in JAX},
+  year = {2023},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  url = {https://github.com/pnkraemer/probdiffeq}
+}
+
+@misc{jax2018github,
+  author =       {James Bradbury and Roy Frostig and Peter Hawkins and Matthew
+                  James Johnson and Chris Leary and Dougal Maclaurin and George
+                  Necula and Adam Paszke and Jake Vander{P}las and Skye
+                  Wanderman-{M}ilne and Qiao Zhang},
+  title =        {{JAX}: composable transformations of {P}ython+{N}um{P}y
+                  programs},
+  version =      {0.2.5},
+  year =         2018,
 }

paper/paper.md

@@ -20,88 +20,36 @@ bibliography: paper.bib
 
 # Summary
 
-The forces on stars, galaxies, and dark matter under external gravitational
-fields lead to the dynamical evolution of structures in the universe. The orbits
-of these bodies are therefore key to understanding the formation, history, and
-future state of galaxies. The field of "galactic dynamics," which aims to model
-the gravitating components of galaxies to study their structure and evolution,
-is now well-established, commonly taught, and frequently used in astronomy.
-Aside from toy problems and demonstrations, the majority of problems require
-efficient numerical tools, many of which require the same base code (e.g., for
-performing numerical orbit integration).
+Probabilistic numerical solvers have emerged as an efficient framework for simulation, uncertainty quantification, and inference in dynamical systems.
+In comparison to traditional numerical methods, which approximate the true trajectory of a system only by a single point estimate, probabilistic numerical solvers compute a _distribution_ over the true, unknown solution of the given differential equation,
+and thereby provide information about the numerical error incurred during the computation.
+ProbNumDiffEq.jl is a Julia package that provides probabilistic numerical solvers for ordinary differential equations (ODEs) and differential algebraic equations (DAEs), within the DifferentialEquations.jl ecosystem [@rackauckas2017differentialequations].
 
-# Statement of need
-
-`Gala` is an Astropy-affiliated Python package for galactic dynamics. Python
-enables wrapping low-level languages (e.g., C) for speed without losing
-flexibility or ease-of-use in the user-interface. The API for `Gala` was
-designed to provide a class-based and user-friendly interface to fast (C or
-Cython-optimized) implementations of common operations such as gravitational
-potential and force evaluation, orbit integration, dynamical transformations,
-and chaos indicators for nonlinear dynamics. `Gala` also relies heavily on and
-interfaces well with the implementations of physical units and astronomical
-coordinate systems in the `Astropy` package [@astropy] (`astropy.units` and
-`astropy.coordinates`).
-
-`Gala` was designed to be used by both astronomical researchers and by
-students in courses on gravitational dynamics or astronomy. It has already been
-used in a number of scientific publications [@Pearson:2017] and has also been
-used in graduate courses on Galactic dynamics to, e.g., provide interactive
-visualizations of textbook material [@Binney:2008]. The combination of speed,
-design, and support for Astropy functionality in `Gala` will enable exciting
-scientific explorations of forthcoming data releases from the *Gaia* mission
-[@gaia] by students and experts alike.
-
-# Mathematics
-
-Single dollars ($) are required for inline mathematics e.g. $f(x) = e^{\pi/x}$
-
-Double dollars make self-standing equations:
-
-$$\Theta(x) = \left\{\begin{array}{l}
-0\textrm{ if } x < 0\cr
-1\textrm{ else}
-\end{array}\right.$$
 
-You can also use plain \LaTeX for equations
-\begin{equation}\label{eq:fourier}
-\hat f(\omega) = \int_{-\infty}^{\infty} f(x) e^{i\omega x} dx
-\end{equation}
-and refer to \autoref{eq:fourier} from text.
 
-# Citations
 
-Citations to entries in paper.bib should be in
-[rMarkdown](http://rmarkdown.rstudio.com/authoring_bibliographies_and_citations.html)
-format.
-
-If you want to cite a software repository URL (e.g. something on GitHub without a preferred
-citation) then you can do it with the example BibTeX entry below for @fidgit.
-
-For a quick reference, the following citation commands can be used:
-- `@author:2001`  ->  "Author et al. (2001)"
-- `[@author:2001]` -> "(Author et al., 2001)"
-- `[@author1:2001; @author2:2001]` -> "(Author1 et al., 2001; Author2 et al., 2002)"
-
-- `@bosch20_calib_adapt_probab_ode_solver`: developed + implemented
-- `@bosch21_pick_and_mix_infor_operat`: developed + implemented
-- `@krämer2021probabilistic`: implemented
-- `@tronarp2022fenrir`: developed + implemented
-- `@bosch2023probabilistic`: developed + implemented
-- `@beck2024diffusion`: developed
-- `@wenger2021probnum`: related software package
-- DALTON: did they compare against my code?
-- BlackBox PN? Probabilistic richardson extrapolation paper?
-- probdiffeq: related code
-
-# Figures
-
-Figures can be included like this:
-![Caption for example figure.\label{fig:example}](figure.png)
-and referenced from text using \autoref{fig:example}.
+# Statement of need
 
-Figure sizes can be customized by adding an optional second parameter:
-![Caption for example figure.](figure.png){ width=20% }
+- Facilitate new probabilistic numerics research for developing new methods within the code base. Examples:
+  @bosch20_calib_adapt_probab_ode_solver
+  @bosch21_pick_and_mix_infor_operat
+  @krämer2021probabilistic
+  @tronarp2022fenrir
+  @bosch2023probabilistic
+  @beck2024diffusion
+- Provide probabilisitc ODE solvers to the broader community:
+  - Baselines to compare against for methods development
+  - Tools for practitioners to use in their work
+
+
+- @bosch20_calib_adapt_probab_ode_solver: developed + implemented
+- @bosch21_pick_and_mix_infor_operat: developed + implemented
+- @krämer2021probabilistic: implemented
+- @tronarp2022fenrir: developed + implemented
+- @bosch2023probabilistic: developed + implemented
+- @beck2024diffusion: developed
+- @wenger2021probnum: related software package
+- @probdiffeq: very related software package in JAX [@jax2018github]
 
 # Acknowledgements
 
@@ -111,15 +59,15 @@ The authors thank the International Max Planck Research School for Intelligent S
 The authors are grateful to Nicholas Krämer for many valuable discussion and to Jonathan Schmidt for feedback on the manuscript.
 
 We acknowledge contributions from 
-Pietro Monticone (@pitmonticone),
-Vedant Puri (@vpuri3),
-Tim Holy (@timholy),
-Daniel González Arribas (@DaniGlez),
-David Widmann (@devmotion),
-Christopher Rackauckas (@ChrisRackauckas),
-Qingyu Qu (@ErikQQY),
-Cornelius Roemer (@corneliusroemer),
-and Jose Storopoli (@storopoli).
+Pietro Monticone (\@pitmonticone),
+Vedant Puri (\@vpuri3),
+Tim Holy (\@timholy),
+Daniel González Arribas (\@DaniGlez),
+David Widmann (\@devmotion),
+Christopher Rackauckas (\@ChrisRackauckas),
+Qingyu Qu (\@ErikQQY),
+Cornelius Roemer (\@corneliusroemer),
+and Jose Storopoli (\@storopoli).
 
 
 # References