DMDpack

"The dynamic mode decomposition (DMD) is an equation-free, data-driven matrix decomposition that is capable of providing accurate reconstructions of spatio-temporal coherent structures arising in nonlinear dynamical systems, or short-time future estimates of such systems. The DMD method provides a regression technique for least-square fitting of video snapshots to a linear dynamical system. The method integrates two of the leading data analysis methods in use today: Fourier transforms and Principal Components. Originally introduced in the fluid mechanics community, DMD traces its origins to Bernard Koopman in 1931 and can be seen as a special case of Koopman theory. Meanwhile DMD has emerged as a powerful tool for analyzing dynamics of nonlinear systems and in the last few years alone, DMD has seen tremendous development in both theory and application. In theory, DMD has seen innovations around compressive architectures, multi-resolution analysis and de-noising algorithms. In addition to continued progress in fluid dynamics, DMD has been applied to new domains, including neuroscience, epidemiology, robotics, and the current application of video processing and computer vision.

The DMDpack includes the following implementations

• Exact DMD (dmd) facilitating truncated, partial or randomized SVD
• Compressed DMD (cdmd)
• Robust DMD (tdmd) using total least squares

https://github.com/Benli11/DMDpack