Added variant names

Author: sichinaga

README.md

@@ -116,27 +116,27 @@ To implement the various versions of the DMD algorithm we follow these works:
 * 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)].
+* **Forward-backward DMD:** 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)].
+* **Total least-squares DMD:** 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)].
+* **Optimal closed-form DMD:** 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)].
+* **Subspace DMD:** 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)].
+* **Physics-informed DMD:** 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)].
+* **Optimized DMD:** 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)].
+* **Bagging, optimized DMD:** 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)].
-* 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
+* **DMD with control:** 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)].
+* **Multiresolution DMD:** 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)].
+* **Sparsity-promoting DMD:** 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)].
+* **Compressed DMD:** 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)].
+* **Randomized DMD:** 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)].
+* **Higher order DMD:** 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)].
+* **HAVOK:** 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)].
+* **Parametric DMD:** 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)].
+* **Extended DMD:** 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)].
+* **LANDO:** 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 Preprocessing
 * 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)]