Dynamical Low-Rank Matrix and Tensor-Network Methods in Fluid Mechanics
Fluid flows exhibit correlated structures across space and time. Dynamical low-rank approximation begins by representing the state variable as a matrix or tensor, extracting and exploiting its evolving correlated structure on the fly to construct efficient reduced-order models directly from the governing equations. Ten...