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] <https://arxiv.org/abs/2004.08424>`__
 
 -  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] <https://arxiv.org/abs/2111.08481>`__
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}