Towards an Adaptive Dynamic Mode Decomposition

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