  The macroscopic forcing method (MFM) of Mani and Park and similar methods for
obtaining turbulence closure operators, such as the Green's function-based
approach of Hamba, recover reduced solution operators from repeated direct
numerical simulations (DNS). MFM has been used to quantify RANS-like operators
for homogeneous isotropic turbulence and turbulent channel flows. Standard
algorithms for MFM force each coarse-scale degree of freedom (i.e., degree of
freedom in the RANS space) and conduct a corresponding fine-scale simulation
(i.e., DNS), which is expensive. We combine this method with an approach
recently proposed by Sch\"afer and Owhadi (2023) to recover elliptic integral
operators from a polylogarithmic number of matrix-vector products. The
resulting Fast MFM introduced in this work applies sparse reconstruction to
expose local features in the closure operator and reconstructs this
coarse-grained differential operator in only a few matrix-vector products and
correspondingly, a few MFM simulations. For flows with significant nonlocality,
the algorithm first "peels" long-range effects with dense matrix-vector
products to expose a local operator. We demonstrate the algorithm's performance
for scalar transport in a laminar channel flow and momentum transport in a
turbulent one. For these, we recover eddy diffusivity operators at 1% of the
cost of computing the exact operator via a brute-force approach for the laminar
channel flow problem and 13% for the turbulent one. We observe that we can
reconstruct these operators with an increase in accuracy by about a factor of
100 over randomized low-rank methods. We glean that for problems in which the
RANS space is reducible to one dimension, eddy diffusivity and eddy viscosity
operators can be reconstructed with reasonable accuracy using only a few
simulations, regardless of simulation resolution or degrees of freedom.
