Towards an Adaptive Dynamic Mode Decomposition rights and content
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Dynamic Mode Decomposition (DMD) is a tool that creates an approximate model from spatio-temporal data. We have developed an architecture of this tool that will adapt to the data from a given problem by leveraging time delay coordinates, projections, and robust principal component analysis. Our scheme which we call Adaptive Dynamic Mode Decomposition (ADMD) can be used in its exact form or the user may even utilize parts of the scheme for generating a DMD model that is more accurate and reliable compared to the one given by standard DMD. ADMD is demonstrated on several datasets of varying complexities and its performance appears to be promising.


Dynamic mode decomposition
Time delay coordinates
Random projection
Discrete fourier transform
Augmented Lagrange multiplier method
Double gyre
Hidden dynamics
Turbulent channel flow

This work is funded by the Office of Research, North South University , under grant number CTRG-19/SEPS/06.