  Numerical simulations in climate, chemistry, or astrophysics are
computationally too expensive for uncertainty quantification or
parameter-exploration at high-resolution. Reduced-order or surrogate models are
multiple orders of magnitude faster, but traditional surrogates are inflexible
or inaccurate and pure machine learning (ML)-based surrogates too data-hungry.
We propose a hybrid, flexible surrogate model that exploits known physics for
simulating large-scale dynamics and limits learning to the hard-to-model term,
which is called parametrization or closure and captures the effect of fine-
onto large-scale dynamics. Leveraging neural operators, we are the first to
learn grid-independent, non-local, and flexible parametrizations. Our
\textit{multiscale neural operator} is motivated by a rich literature in
multiscale modeling, has quasilinear runtime complexity, is more accurate or
flexible than state-of-the-art parametrizations and demonstrated on the chaotic
equation multiscale Lorenz96.
