Utilizing Mixed Training and Multi-Head Attention to Address Data Shift in AI-Based Electromagnetic Solvers for Nano-Structured Metamaterials

Utilizing Mixed Training and Multi-Head Attention to Address Data Shift in AI-Based Electromagnetic Solvers for Nano-Structured Metamaterials

When designing nano-structured metamaterials with an iterative optimization method, a fast deep learning solver is desirable to replace a time-consuming numerical solver, and the related issue of data shift is a subtle yet easily overlooked challenge. In this work, we explore the data shift challeng...

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Bibliographic Details
Main Authors: Zhenjia Zeng, Lei Wang, Yiran Wu, Zhipeng Hu, Julian Evans, Xinhua Zhu, Gaoao Ye, Sailing He
Format: Article
Language:English
Published: MDPI AG 2023-10-01
Series:Nanomaterials
Subjects:
data shift
mixed training
multi-head attention
AI-based electromagnetic solvers
nano-structured metamaterials
Online Access:https://www.mdpi.com/2079-4991/13/20/2778

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https://www.mdpi.com/2079-4991/13/20/2778

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