[PRE REVIEW]: PySINDy: A Python package for the sparse identification of nonlinear dynamical systems from data

[whedon]

Submitting author: @briandesilva (Brian de Silva) Repository: https://github.com/dynamicslab/pysindy Version: v0.12.0 Editor: @terrytangyuan Reviewers: @sixpearls, @dawbarton Managing EiC: Daniel S. Katz

Author instructions

Thanks for submitting your paper to JOSS @briandesilva. Currently, there isn't an JOSS editor assigned to your paper.

@briandesilva if you have any suggestions for potential reviewers then please mention them here in this thread (without tagging them with an @). In addition, this list of people have already agreed to review for JOSS and may be suitable for this submission (please start at the bottom of the list).

Editor instructions

The JOSS submission bot @whedon is here to help you find and assign reviewers and start the main review. To find out what @whedon can do for you type:

@whedon commands

[whedon]

Hello human, I'm @whedon, a robot that can help you with some common editorial tasks.

For a list of things I can do to help you, just type:

@whedon commands

For example, to regenerate the paper pdf after making changes in the paper's md or bib files, type:

@whedon generate pdf

[whedon]

Software report (experimental):

github.com/AlDanial/cloc v 1.84  T=0.27 s (156.2 files/s, 24452.5 lines/s)
-------------------------------------------------------------------------------
Language                     files          blank        comment           code
-------------------------------------------------------------------------------
Python                          27            666           1025           1839
Jupyter Notebook                 2              0           1693            575
TeX                              1             24              0            218
Markdown                         3             29              0            109
Ruby                             1             28             12            106
YAML                             4              8              0            103
reStructuredText                 2             35             19             48
JSON                             1              0              0             22
TOML                             1              2              0             16
-------------------------------------------------------------------------------
SUM:                            42            792           2749           3036
-------------------------------------------------------------------------------

Statistical information for the repository '2093' was gathered on 2020/02/11.
The following historical commit information, by author, was found:

Author                     Commits    Insertions      Deletions    % of changes
Brian                            2             1              2            0.01
Brian de Silva                   2            77             12            0.25
Markus Quade                    68          2188            974            8.72
Taren Gorman                     1            15              0            0.04
TarenGorman                      3            49              6            0.15
Thomas Isele                     1            30              2            0.09
Thomasillo                       1           100              2            0.28
briandesilva                    70          9558          12832           61.74
kpchamp                         39          1849            325            5.99
mq                              97          6053           2190           22.73

Below are the number of rows from each author that have survived and are still
intact in the current revision:

Author                     Rows      Stability          Age       % in comments
Markus Quade                262           12.0          3.4                6.49
TarenGorman                   2            4.1         25.9                0.00
briandesilva               2194           23.0          1.7                5.10
kpchamp                    1177           63.7          2.0                6.71
mq                           41            0.7         30.9                0.00

[whedon]

Reference check summary:

OK DOIs

- None

MISSING DOIs

- https://doi.org/10.1073/pnas.1517384113 may be missing for title: Discovering governing equations from data by sparse identification of nonlinear dynamical systems
- https://doi.org/10.1109/access.2018.2886528 may be missing for title: A unified framework for sparse relaxed regularized regression: SR3
- https://doi.org/10.1126/science.1165893 may be missing for title: Distilling Free-Form Natural Laws from Experimental Data
- https://doi.org/10.1103/physrevmaterials.2.083802 may be missing for title: SISSO: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates
- https://doi.org/10.1364/oe.24.030433 may be missing for title: Sparse identification for nonlinear optical communication systems: SINO method
- https://doi.org/10.1063/1.5066099 may be missing for title: Reactive SINDy: Discovering governing reactions from concentration data
- https://doi.org/10.1063/1.4977057 may be missing for title: Sparse identification of a predator-prey system from simulation data of a convection model
- https://doi.org/10.1016/j.ymssp.2018.08.033 may be missing for title: Sparse structural system identification method for nonlinear dynamic systems with hysteresis/inelastic behavior
- https://doi.org/10.1098/rspa.2016.0446 may be missing for title: Learning partial differential equations via data discovery and sparse optimization
- https://doi.org/10.1137/16m1086637 may be missing for title: Exact recovery of chaotic systems from highly corrupted data
- https://doi.org/10.1137/18m116798x may be missing for title: Extracting sparse high-dimensional dynamics from limited data
- https://doi.org/10.1017/jfm.2017.823 may be missing for title: Constrained Sparse Galerkin Regression
- https://doi.org/10.1103/physreve.96.023302 may be missing for title: Sparse model selection via integral terms
- https://doi.org/10.1016/j.jcp.2018.10.045 may be missing for title: Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- https://doi.org/10.1016/j.jcp.2019.07.049 may be missing for title: Sparse identification of truncation errors
- https://doi.org/10.1103/physreve.101.010203 may be missing for title: Using noisy or incomplete data to discover models of spatiotemporal dynamics

INVALID DOIs

- None

[whedon]

:point_right: Check article proof :page_facing_up: :point_left:

[danielskatz]

@briandesilva - it looks like you may need to add a bunch of DOIs to your references (although whedon isn't always 100% correct on its guesses) - see the example submission and references for how to add DOIs. Once you have done this, regenerate the paper by entering @whedon generate pdf as a new comment here

[danielskatz]

@whedon invite @terrytangyuan as editor

[whedon]

@terrytangyuan has been invited to edit this submission.

[terrytangyuan]

@whedon assign @terrytangyuan as editor

[whedon]

OK, the editor is @terrytangyuan

[briandesilva]

@whedon generate pdf

[whedon]

:point_right: Check article proof :page_facing_up: :point_left:

[terrytangyuan]

@briandesilva If you have any suggestions for potential reviewers then please mention them here in this thread (without tagging them with an @). In addition, this list of people have already agreed to review for JOSS and may be suitable for this submission.

[briandesilva]

From the list you provided, the following people seem like appropriate potential reviewers:

• jmbr
• dawbarton
• sixpearls
• MakrinaAgaoglou

[terrytangyuan]

👋 Hi @jmbr @dawbarton @sixpearls @MakrinaAgaoglou, if you would like to review for this submission, please let us know here! We need at least two reviewers.

[sixpearls]

Sure, I can review

[dawbarton]

I'm happy to review but I've got a heavy teaching load for the next 3 weeks so it'll have to be after that.

[terrytangyuan]

@whedon assign @sixpearls as reviewer

[whedon]

OK, @sixpearls is now a reviewer

[terrytangyuan]

@whedon add @dawbarton as reviewer

[whedon]

OK, @dawbarton is now a reviewer

[terrytangyuan]

I'm happy to review but I've got a heavy teaching load for the next 3 weeks so it'll have to be after that.

Thanks @dawbarton this is fine. @sixpearls can probably review first and then you can start reviewing once you are available.

[terrytangyuan]

@whedon start review

[whedon]

OK, I've started the review over in https://github.com/openjournals/joss-reviews/issues/2104.

[terrytangyuan]

Looking forward to your review in #2104!