MetaNOR: A Meta-Learnt Nonlocal Operator Regression Approach for Metamaterial Modeling
This work addresses the challenge of quickly building surrogate models for new metamaterial tasks, which is incremental as it builds on existing meta-learning and nonlocal operator methods.
The paper tackled the problem of efficiently modeling wave propagation in metamaterials with different microstructures by proposing MetaNOR, a meta-learnt approach for transfer-learning operators, resulting in substantial improvements in sampling efficiency for new materials.
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.