Added references to README.md

Author: sichinaga

README.md

@@ -111,17 +111,35 @@ First prize winner in **DSWeb 2019 Contest** _Tutorials on Dynamical Systems Sof
 ## References
 To implement the various versions of the DMD algorithm we follow these works:
 
+### General DMD References
 * Kutz, Brunton, Brunton, Proctor. *Dynamic Mode Decomposition: Data-Driven Modeling of Complex Systems*. SIAM Other Titles in Applied Mathematics, 2016. [[DOI](https://doi.org/10.1137/1.9781611974508)] [[bibitem](readme/refs/Kutz2016_1.bib)].
-* Gavish, Donoho. *The optimal hard threshold for singular values is 4/sqrt(3)*. IEEE Transactions on Information Theory, 2014. [[DOI](https://doi.org/10.1109/TIT.2014.2323359)] [[bibitem](readme/refs/Gavish2014.bib)].
-* Matsumoto, Indinger. *On-the-fly algorithm for Dynamic Mode Decomposition using Incremental Singular Value Decomposition and Total Least Squares*. 2017. [[arXiv](https://arxiv.org/abs/1703.11004)] [[bibitem](readme/refs/Matsumoto2017.bib)].
-* Hemati, Rowley, Deem, Cattafesta. *De-biasing the dynamic mode decomposition for applied Koopman spectral analysis of noisy datasets*. Theoretical and Computational Fluid Dynamics, 2017. [[DOI](https://doi.org/10.1007/s00162-017-0432-2)] [[bibitem](readme/refs/Hemati2017.bib)].
+* Brunton, Budišić, Kaiser, Kutz. *Modern Koopman Theory for Dynamical Systems*. SIAM Review, 2022. [[DOI](https://doi.org/10.1137/21M1401243)] [[bibitem](readme/refs/Brunton2022.bib)].
+
+### DMD Variants: Noise-robust Methods
 * Dawson, Hemati, Williams, Rowley. *Characterizing and correcting for the effect of sensor noise in the dynamic mode decomposition*. Experiments in Fluids, 2016. [[DOI](https://doi.org/10.1007/s00348-016-2127-7)] [[bibitem](readme/refs/Dawson2016.bib)].
+* Hemati, Rowley, Deem, Cattafesta. *De-biasing the dynamic mode decomposition for applied Koopman spectral analysis of noisy datasets*. Theoretical and Computational Fluid Dynamics, 2017. [[DOI](https://doi.org/10.1007/s00162-017-0432-2)] [[bibitem](readme/refs/Hemati2017.bib)].
+* Héas, Herzet. *Low-rank dynamic mode decomposition: An exact and tractable solution*. Journal of Nonlinear Science, 2022. [[DOI](https://doi.org/10.1007/s00332-021-09770-w)] [[bibitem](readme/refs/Heas2022.bib)].
+* Takeishi, Kawahara, Yairi. *Subspace dynamic mode decomposition for stochastic Koopman analysis*. Physical Review E, 2017. [[DOI](https://doi.org/10.1103/PhysRevE.96.033310)] [[bibitem](readme/refs/Takeishi2017.bib)].
+* Baddoo, Herrmann, McKeon, Kutz, Brunton. *Physics-informed dynamic mode decomposition*. Proceedings of the Royal Society A, 2023. [[DOI](https://doi.org/10.1098/rspa.2022.0576)] [[bibitem](readme/refs/Baddoo2023.bib)].
+* Askham, Kutz. *Variable projection methods for an optimized dynamic mode decomposition*. SIAM Journal on Applied Dynamical Systems, 2018. [[DOI](https://doi.org/10.1137/M1124176)] [[bibitem](readme/refs/Askham2018.bib)].
+* Sashidhar, Kutz. *Bagging, optimized dynamic mode decomposition for robust, stable forecasting with spatial and temporal uncertainty quantification*. Proceedings of the Royal Society A, 2022. [[DOI](https://doi.org/10.1098/rsta.2021.0199)] [[bibitem](readme/refs/Sashidhar2022.bib)].
+
+### DMD Variants: Additional Methods and Extensions
+* Proctor, Brunton, Kutz. *Dynamic mode decomposition with control*. SIAM Journal on Applied Dynamical Systems, 2016. [[DOI](https://doi.org/10.1137/15M1013857)] [[bibitem](readme/refs/Proctor2016.bib)].
 * Kutz, Fu, Brunton. *Multiresolution Dynamic Mode Decomposition*. SIAM Journal on Applied Dynamical Systems, 2016. [[DOI](https://doi.org/10.1137/15M1023543)] [[bibitem](readme/refs/Kutz2016_2.bib)].
+* Jovanović, Schmid, Nichols *Sparsity-promoting dynamic mode decomposition*. Physics of Fluids, 2014. [[DOI](https://doi.org/10.1063/1.4863670)] [[bibitem](readme/refs/Jovanovic2014.bib)].
 * Erichson, Brunton, Kutz. *Compressed dynamic mode decomposition for background modeling*. Journal of Real-Time Image Processing, 2016. [[DOI](https://doi.org/10.1007/s11554-016-0655-2)] [[bibitem](readme/refs/Erichson2016.bib)].
+* Erichson, Mathelin, Kutz, Brunton. *Randomized dynamic mode decomposition*. SIAM Journal on Applied Dynamical Systems, 2019. [[DOI](https://doi.org/10.1137/18M1215013)] [[bibitem](readme/refs/Erichson2019.bib)].
 * Le Clainche, Vega. *Higher Order Dynamic Mode Decomposition*. Journal on Applied Dynamical Systems, 2017. [[DOI](https://doi.org/10.1137/15M1054924)] [[bibitem](readme/refs/LeClainche2017.bib)].
+* Brunton, Brunton, Proctor, Kaiser, Kutz. *Chaos as an intermittently forced linear system*. Nature Communications, 2017. [[DOI](https://doi.org/10.1038/s41467-017-00030-8)] [[bibitem](readme/refs/Brunton2017.bib)].
 * Andreuzzi, Demo, Rozza. *A dynamic mode decomposition extension for the forecasting of parametric dynamical systems*. 2021.  [[DOI](https://doi.org/10.1137/22M1481658)] [[bibitem](readme/refs/Andreuzzi2021.bib)].
-* Jovanović, Schmid, Nichols *Sparsity-promoting dynamic mode decomposition*. 2014.  [[arXiv](https://arxiv.org/abs/1309.4165)] [[bibitem](readme/refs/Jovanovic2014.bib)].
+* Williams, Rowley, Kevrekidis. *A kernel-based method for data-driven koopman spectral analysis*. Journal of Computational Dynamics, 2015. [[DOI](https://doi.org/10.3934/jcd.2015005)] [[bibitem](readme/refs/Williams2015.bib)].
+* Baddoo, Herrmann, McKeon, Brunton. *Kernel learning for robust dynamic mode decomposition: linear and nonlinear disambiguation optimization*. Proceedings of the Royal Society A, 2022. [[DOI](https://doi.org/10.1098/rspa.2021.0830)] [[bibitem](readme/refs/Baddoo2022.bib)].
 
+### Implementation Tools and Preprocessors
+* Gavish, Donoho. *The optimal hard threshold for singular values is 4/sqrt(3)*. IEEE Transactions on Information Theory, 2014. [[DOI](https://doi.org/10.1109/TIT.2014.2323359)] [[bibitem](readme/refs/Gavish2014.bib)].
+* Matsumoto, Indinger. *On-the-fly algorithm for Dynamic Mode Decomposition using Incremental Singular Value Decomposition and Total Least Squares*. 2017. [[arXiv](https://arxiv.org/abs/1703.11004)] [[bibitem](readme/refs/Matsumoto2017.bib)].
+* Hirsh, Harris, Kutz, Brunton. *Centering data improves the dynamic mode decomposition*. SIAM Journal on Applied Dynamical Systems, 2020. [[DOI](https://doi.org/10.1137/19M1289881)] [[bibitem](readme/refs/Hirsh2020.bib)]
 
 ### Recent works using PyDMD
 You can find a list of the scientific works using **PyDMD** [here](https://scholar.google.com/scholar?oi=bibs&hl=en&cites=5544023489671534143).