Abstract
In recent years, deep learning has risen to the forefront of many fields, overcoming challenges previously considered difficult to solve by traditional methods. In the field of metamaterials, there are significant challenges in the design and optimization of metamaterials, including the need for a large number of labeled data sets and one-to-many mapping when solving inverse problems. Here, we will use deep learning methods to build a metamaterial database to achieve rapid design and analysis methods of metamaterials. These technologies have significantly improved the feasibility of more complex metamaterial designs and provided new metamaterial design and analysis ideas.
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