Abstract
The unprecedented availability of high-fidelity data measurements in various disciplines of engineering and physical and medical sciences reinforces the development of more sophisticated algorithms for data processing and analysis. More advanced algorithms are required to extract the spatiotemporal features concealed in the data that represent the system dynamics. Usage of advanced data-driven algorithms paves the way to understand the associated dominant dynamical behavior and, thus, improves the capacity for various tasks, such as forecasting, control, and modal analysis. One such emerging method for data-driven analysis is dynamic mode decomposition (DMD). The algorithm for DMD is introduced by Peter J. Schmid in 2010 based on the foundation of Koopman operator (Schmid. J Fluid Mech 656:5–28, 2010). It is basically a decomposition algorithm with intelligence to identify the spatial patterns and temporal features of the data measurements. DMD has recently gained improved interest due to its dominant ability to mine meaningful information from available measurements. It has revolutionized the analysis and modeling of physical systems like fluid dynamics, neuroscience, financial trading markets, multimedia, smart grid, etc. The ability to recognize the spatiotemporal patterns makes DMD as prominent among other similar algorithms. DMD algorithm merges the characteristics of proper orthogonal decomposition (POD) and Fourier transform.
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Schmid, P. J. (2010). Dynamic mode decomposition of numerical and experimental data. Journal of Fluid Mechanics, 656, 5–28.
Tu, J. H., Rowley, C. W., Luchtenburg, D. M., Brunton, S. L., & Kutz, J. N. (2014). On dynamic mode decomposition: Theory and applications. Journal of Computational Dynamics, 1, 391–421.
Kutz, J. N., Brunton, S. L., Brunton, B. W., & Proctor, J. L. (2016). Dynamic mode decomposition: Data-driven modeling of complex systems. SIAM.
Kumar, S. S., Manjusha, K., & Soman, K. P. (2014). Novel SVD based character recognition approach for Malayalam language script. In Recent advances in intelligent informatics (pp. 435–442). Cham: Springer.
Kutz, J. N., Fu, X., & Brunton, S. L. (2016). Multiresolution dynamic mode decomposition. SIAM Journal on Applied Dynamical Systems, 15(2), 713–735.
Kumar, S. S., Anand Kumar, M., Soman, K. P., & Poornachandran, P. (2020). Dynamic mode-based feature with random mapping for sentiment analysis. In Intelligent systems, technologies and applications (pp. 1–15). Singapore: Springer.
Barocio, E., Pal, B. C., Thornhill, N. F., & Messina, A. R. (2014). A dynamic mode decomposition framework for global power system oscillation analysis. IEEE Transactions on Power Systems, 30(6), 2902–2912.
Saldaña, A. E., Barocio, E., Messina, A. R., Ramos, J. J., Juan Segundo, R., & Tinajero, G. A. (2017). Monitoring harmonic distortion in microgrids using dynamic mode decomposition. In 2017 IEEE Power & Energy Society General Meeting (pp. 1–5). IEEE.
Mohan, N., Soman, K. P., & Sachin, K. S. (2018). A data-driven approach for estimating power system frequency and amplitude using dynamic mode decomposition. In 2018 International Conference and Utility Exhibition on Green Energy for Sustainable Development (ICUE) (pp. 1–9). IEEE.
Mohan, N., & Soman, K. P. (2018). Power system frequency and amplitude estimation using variational mode decomposition and chebfun approximation system. In 2018 Twenty Fourth National Conference on Communications (NCC) (pp. 1–6). IEEE.
Prakash, L., Neethu Mohan, S., Kumar, S., & Soman, K. P. (2016). Accurate frequency estimation method based on basis approach and empirical wavelet transform. In Proceedings of the Second International Conference on Computer and Communication Technologies (pp. 801–809). New Delhi: Springer.
Soman, K. P., Sachin Kumar, S., Mohan, N., & Poornachandran, P. (2019). Modern methods for signal analysis and its applications. In Recent advances in computational intelligence (pp. 263–290). Cham: Springer.
Akshay, S., Gopalakrishnan, E. A., & Soman, K. P. (In Press). Investigating the effectiveness of DMD and its variants for complex data analysis. In International Conference on Intelligent Computing and Control Systems – ICCS 2019. IEEE.
Kuttichira, D. P., Gopalakrishnan, E. A., Menon, V. K., & Soman, K. P. (2017, September). Stock price prediction using dynamic mode decomposition. In 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (pp. 55–60). IEEE.
Mohan, N., Soman, K. P., & Kumar, S. S. (2018). A data-driven strategy for short-term electric load forecasting using dynamic mode decomposition model. Applied Energy, 232, 229–244.
Sikha, O. K., Sachin Kumar, S., & Soman, K. P. (2018). Salient region detection and object segmentation in color images using dynamic mode decomposition. Journal of Computational Science, 25, 351–366.
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Akshay, S., Soman, K.P., Mohan, N., Sachin Kumar, S. (2021). Dynamic Mode Decomposition and Its Application in Various Domains: An Overview. In: Kumar, R., Paiva, S. (eds) Applications in Ubiquitous Computing. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-35280-6_6
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