From 02805e2082f28688df1b69710e050ef8b1aaacda Mon Sep 17 00:00:00 2001 From: Mehmet Hakan Satman Date: Fri, 27 Sep 2024 21:16:08 +0300 Subject: [PATCH] Minor fix on paper and bibtex (#330) * update bibtex * update paper.md * Update paper/paper.md --------- Co-authored-by: Nathanael Bosch --- paper/paper.bib | 8 ++++---- paper/paper.md | 4 ++-- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/paper/paper.bib b/paper/paper.bib index 823ed1505..b7f7ff5b8 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -56,7 +56,7 @@ @inproceedings{beck2024diffusion } @misc{wenger2021probnum, - title = {ProbNum: Probabilistic Numerics in Python}, + title = {ProbNum: Probabilistic Numerics in {P}ython}, author = {Jonathan Wenger and Nicholas Krämer and Marvin Pförtner and Jonathan Schmidt and Nathanael Bosch and Nina Effenberger and Johannes Zenn and Alexandra Gessner and Toni Karvonen and @@ -87,8 +87,8 @@ @InProceedings{dalton2024 } @article{rackauckas2017differentialequations, - title = {{DifferentialEquations.jl} – A Performant and Feature-Rich - Ecosystem for Solving Differential Equations in Julia}, + title = {{DifferentialEquations.jl} – {A} Performant and Feature-Rich + Ecosystem for Solving Differential Equations in {J}ulia}, author = {Rackauckas, Christopher and Nie, Qing}, journal = {Journal of Open Research Software}, volume = 5, @@ -155,7 +155,7 @@ @article{tronarp19 @article{kersting20, author = {Kersting, Hans and Sullivan, T. J. and Hennig, Philipp}, - title = {Convergence rates of Gaussian ODE filters}, + title = {Convergence rates of {G}aussian ODE filters}, journal = {Statistics and Computing}, year = 2020, month = {Nov}, diff --git a/paper/paper.md b/paper/paper.md index feb4f9fc5..c77da7ed0 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -43,7 +43,7 @@ ProbNumDiffEq.jl implements many of the methods referenced above and packages th # Statement of need Filtering-based probabilistic numerical ODE solvers have been an active field of research for the past decade, but their application in practical simulation and inference problems has been limited. -ProbNumDiffEq.jl aims to help close this gap. +ProbNumDiffEq.jl aims to bridge this gap. ProbNumDiffEq.jl implements probabilistic numerical methods as performant, documented, and easy-to-use ODE solvers inside the well-established DifferentialEquations.jl ecosystem [@rackauckas2017differentialequations]. Thereby, the package benefits from the extensive testing, documentation, performance optimization, and functionality that DifferentialEquations.jl provides. Users can easily find help and examples regarding many features that are not particular to ProbNumDiffEq.jl in the DifferentialEquations.jl documentation, @@ -61,7 +61,7 @@ We also hope that by providing documented and performant implementations of publ ProbNumDiffEq.jl is also the only software package in Julia, at the time of writing, that provides a comprehensive set of probabilistic numerical ODE solvers. Outside of Julia, two other software packages provide a similar functionality. ProbNum [@wenger2021probnum] -is a Python package that implements probabilistic numerical for various numerical problems, including linear systems, quadrature, and ODEs. +is a Python package that implements probabilistic numerical methods for various numerical problems, including linear systems, quadrature, and ODEs. ProbNum particularly aims to facilitate rapid experimentation and accelerate the development of new methods [@wenger2021probnum]. It is therefore broader in scope and provides functionality not covered by ProbNumDiffEq.jl. But, it also lacks some of the specialized ODE solvers available in ProbNumDiffEq.jl, and with its reliance on Python and NumPy [@numpy] and the lack of just-in-time compilation, it is also generally less performant.