From 05e710bf328f7554c2eea9cede5409c923918ebd Mon Sep 17 00:00:00 2001 From: Alan Kaptanoglu <37751714+akaptano@users.noreply.github.com> Date: Wed, 5 Jan 2022 21:13:16 -0800 Subject: [PATCH 1/2] Added Zach to new PySINDy JOSS paper author list --- README.rst | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/README.rst b/README.rst index 2e1ca85c6..92a4eebff 100644 --- a/README.rst +++ b/README.rst @@ -215,7 +215,7 @@ If you use PySINDy in your work, please cite it using the following two referenc Brian M. de Silva, Kathleen Champion, Markus Quade, Jean-Christophe Loiseau, J. Nathan Kutz, and Steven L. Brunton., (2020). *PySINDy: A Python package for the sparse identification of nonlinear dynamical systems from data.* Journal of Open Source Software, 5(49), 2104, https://doi.org/10.21105/joss.02104 -Alan A. Kaptanoglu, Brian M. de Silva, Urban Fasel, Kadierdan Kaheman, Jared L. Callaham, Charles B. Delahunt, Kathleen Champion, Jean-Christophe Loiseau,J. Nathan Kutz, and Steven L. Brunton. *PySINDy: A comprehensive Python packagefor robust sparse system identification.* arXiv preprint arXiv:2111.08481, 2021. +Alan A. Kaptanoglu, Brian M. de Silva, Urban Fasel, Kadierdan Kaheman, Jared L. Callaham, Charles B. Delahunt, Zachary G. Nicolaou, Kathleen Champion, Jean-Christophe Loiseau,J. Nathan Kutz, and Steven L. Brunton. *PySINDy: A comprehensive Python packagefor robust sparse system identification.* arXiv preprint arXiv:2111.08481, 2021. Bibtex: @@ -240,7 +240,7 @@ Bibtex: @article{kaptanoglu2021pysindy, title={PySINDy: A comprehensive Python package for robust sparse system identification}, - author={Alan A. Kaptanoglu and Brian M. de Silva and Urban Fasel and Kadierdan Kaheman and Jared L. Callaham and Charles B. Delahunt and Kathleen Champion and Jean-Christophe Loiseau and J. Nathan Kutz and Steven L. Brunton}, + author={Alan A. Kaptanoglu and Brian M. de Silva and Urban Fasel and Kadierdan Kaheman and Jared L. Callaham and Charles B. Delahunt and Zachary G. Nicolaou and Kathleen Champion and Jean-Christophe Loiseau and J. Nathan Kutz and Steven L. Brunton}, year={2021}, Journal = {arXiv preprint arXiv:2111.08481}, } @@ -254,8 +254,8 @@ References `[arXiv] `__ - Kaptanoglu, Alan A., Brian M. de Silva, Urban Fasel, Kadierdan Kaheman, - Jared L. Callaham, Charles B. Delahunt, Kathleen Champion, Jean-Christophe Loiseau, - J. Nathan Kutz, and Steven L. Brunton. + Jared L. Callaham, Charles B. Delahunt, Zachary G. Nicolaou, Kathleen Champion, + Jean-Christophe Loiseau, J. Nathan Kutz, and Steven L. Brunton. *PySINDy: A comprehensive Python package for robust sparse system identification.* arXiv preprint arXiv:2111.08481 (2021). `[arXiv] `__ From 889496b5870cba9c9a772461d52d6e9f398698dc Mon Sep 17 00:00:00 2001 From: Alan Kaptanoglu <37751714+akaptano@users.noreply.github.com> Date: Wed, 5 Jan 2022 21:19:35 -0800 Subject: [PATCH 2/2] Added Zach as an author in the new JOSS paper.md file --- docs/JOSS2/paper.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/docs/JOSS2/paper.md b/docs/JOSS2/paper.md index 47d0994ff..8e9aa93b8 100644 --- a/docs/JOSS2/paper.md +++ b/docs/JOSS2/paper.md @@ -20,6 +20,8 @@ authors: affiliation: 3 - name: Charles B. Delahunt affiliation: 2 + - name: Zachary G. Nicolaou + affiliation: 2 - name: Kathleen Champion affiliation: 2 - name: Jean-Christophe Loiseau @@ -81,7 +83,7 @@ Recent variants of the SINDy method are available that address systems with cont In order to incorporate these new developments and accommodate the wide variety of possible dynamical systems, we have extended `PySINDy` to a more general setting and added significant new functionality. Our code\footnote{\url{https://github.com/dynamicslab/pysindy}} is thoroughly documented, contains extensive examples, and integrates a wide range of functionality, some of which may be found in a number of other local SINDy implementations\footnote{\url{https://github.com/snagcliffs/PDE-FIND}, \url{https://github.com/eurika-kaiser/SINDY-MPC},\\ \url{https://github.com/dynamicslab/SINDy-PI}, \url{https://github.com/SchatzLabGT/SymbolicRegression},\\ \url{https://github.com/dynamicslab/databook_python}, \url{https://github.com/sheadan/SINDy-BVP},\\ \url{https://github.com/sethhirsh/BayesianSindy}, \url{https://github.com/racdale/sindyr},\\ \url{https://github.com/SciML/DataDrivenDiffEq.jl}, \url{https://github.com/MathBioCU/WSINDy_PDE},\\ \url{https://github.com/pakreinbold/PDE_Discovery_Weak_Formulation}, \url{https://github.com/ZIB-IOL/CINDy}}. In contrast to some of these existing implementations, `PySINDy` is completely open-source, professionally-maintained (for instance, providing unit tests and adhering to PEP8 stylistic standards), and minimally dependent on non-standard Python packages. # New features -Given spatiotemporal data $\mathbf{Q}(\mathbf{x}, t) \in \mathbb{R}^{m\times n}$, and optional control inputs $\mathbf{u} \in \mathbb{R}^{m \times r}$ (note $m$ has been redefined here to be the product of the number of spatial measurements and the number of time samples), `PySINDy` can now approximate algebraic systems of PDEs (and corresponding weak forms) in up to 3 spatial dimensions. Assuming the system is described by a function $\mathbf{g}$, we have +Given spatiotemporal data $\mathbf{Q}(\mathbf{x}, t) \in \mathbb{R}^{m\times n}$, and optional control inputs $\mathbf{u} \in \mathbb{R}^{m \times r}$ (note $m$ has been redefined here to be the product of the number of spatial measurements and the number of time samples), `PySINDy` can now approximate algebraic systems of PDEs (and corresponding weak forms) in an arbitrary number of spatial dimensions. Assuming the system is described by a function $\mathbf{g}$, we have \begin{equation}\label{eq:pysindy_eq} \mathbf{g}(\mathbf{q},\mathbf q_t, \mathbf q_x, \mathbf q_y, \mathbf q_{xx}, ..., \mathbf{u}) = 0. \end{equation}