Physics-Informed Deep Learning for Time-Domain Electromagnetic Radiation Problem

被引：0

作者：

Ge, Yingze ^{[1
]}

Guo, Liangshuai ^{[1
]}

Li, Maokun ^{[1
]}

机构：

[1] Tsinghua Univ, Beijing Natl Res Ctr Informat Sci & Technol, Dept Elect Engn, Beijing 100084, Peoples R China

来源：

2022 IEEE MTT-S INTERNATIONAL MICROWAVE BIOMEDICAL CONFERENCE (IMBIOC) | 2022年

基金：

中国国家自然科学基金;

关键词：

computational electromagnetics; electromagnetic radiation; machine learning; physics-informed nerual networks;

D O I：

10.1109/IMBIOC52515.2022.9790302

中图分类号：

Q6 [生物物理学];

学科分类号：

071011 ;

摘要：

We explore the application of physics-informed deep learning to solve time-domain electromagnetic problems. This method takes advantage of the differentiability of neural networks and fully integrated with first principles. Compared to traditional approach, there is no need of discretization. Numerical experiment verifies the accuracy of this scheme.

引用

页码：114 / 116

页数：3

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