Assess the effect of LLA-RPCA preprocessing on dynamical analyses

Assess the effects of library-learning-assisted robust principal component analysis preprocessing on dynamic mode decomposition and reduced-order modeling.

Background

The paper evaluates LLA-RPCA primarily through reconstruction errors, POD-subspace recovery, and derived-vorticity fields. It does not determine whether preprocessing with LLA-RPCA improves or alters other downstream analyses, including dynamic mode decomposition and reduced-order modeling. Establishing these effects would clarify the broader utility and possible limitations of the method for data-driven flow analysis.