论文标题
Metanor：用于超材料建模的元学习非局部运算符回归方法
MetaNOR: A Meta-Learnt Nonlocal Operator Regression Approach for Metamaterial Modeling
论文作者
论文摘要
我们提出了Metanor，这是一种基于非局部操作员回归的转移学习操作员的元学习方法。总体目标是有效地为具有不同微观结构的新的和未知的材料学习任务提供替代模型。该算法由两个阶段组成：（1）从现有任务中学习常见的非本地内核表示； （2）通过不同的材料转移学习的知识，并迅速学习替代操作员，以进行看不见的任务，其中只需要少数测试样本。我们应用元人对1D超材料内的波传播进行建模，显示了新材料的采样效率的实质性提高。
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.
