  We propose MetaNOR, a meta-learnt approach for transfer-learning operators
based on the nonlocal operator regression. The overall goal is to efficiently
provide surrogate models for new and unknown material-learning tasks with
different microstructures. The algorithm consists of two phases: (1) learning a
common nonlocal kernel representation from existing tasks; (2) transferring the
learned knowledge and rapidly learning surrogate operators for unseen tasks
with a different material, where only a few test samples are required. We apply
MetaNOR to model the wave propagation within 1D metamaterials, showing
substantial improvements on the sampling efficiency for new materials.
