RCNN: Recurrent Convolutional Neural Network for Solving 3-D Wave Equation-Reference-Cited by-同舟云学术

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RCNN: Recurrent Convolutional Neural Network for Solving 3-D Wave Equation

Published:2023 Issue: Volume:20 Page:1-5
ISSN:1545-598X
Container-title:IEEE Geoscience and Remote Sensing Letters
language:
Short-container-title:IEEE Geosci. Remote Sensing Lett.

Author:

Li Chenxi¹,Ji Duofa¹^ORCID,Zhai Changhai¹^ORCID,Cao Zelin²,Ma Yuhong³

Affiliation:

1. Key Laboratory of Structures Dynamic Behavior and Control of the Ministry of Education, Harbin Institute of Technology, Harbin, China

2. School of Civil Engineering, Hebei University of Engineering, Handan, China

3. Earthquake Engineering Research and Test Center, Guangzhou University, Guangzhou, China

Funder

National Natural Science Foundation of China

Open Research Fund Program of Guangdong Key Laboratory of Earthquake Engineering and Application Technology

Funds for Creative Research Groups of National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Geotechnical Engineering and Engineering Geology

Link

http://xplorestaging.ieee.org/ielx7/8859/10034981/10304222.pdf?arnumber=10304222

Reference22 articles.

1. Deep learning for fast simulation of seismic waves in complex media

2. Prediction of Ground-Motion Parameters for the NGA-West2 Database Using Refined Second-Order Deep Neural Networks

3. Physics informed machine learning: Seismic wave equation

4. Physics-Informed Deep Neural Network for Inhomogeneous Magnetized Plasma Parameter Inversion

5. A perfectly matched layer absorbing boundary condition for the second-order seismic wave equation

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