Multiscale Neural Operator: Learning Fast and Grid-independent PDE
Solvers
- URL: http://arxiv.org/abs/2207.11417v1
- Date: Sat, 23 Jul 2022 05:01:03 GMT
- Title: Multiscale Neural Operator: Learning Fast and Grid-independent PDE
Solvers
- Authors: Bj\"orn L\"utjens, Catherine H. Crawford, Campbell D Watson,
Christopher Hill, Dava Newman
- Abstract summary: We propose a hybrid, flexible surrogate model that exploits known physics for simulating large-scale dynamics.
We are the first to learn grid-independent, non-local, and flexible parametrizations.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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