Direct Imaging Using Physics Informed Neural Networks-Reference-Cited by-同舟云学术

Direct Imaging Using Physics Informed Neural Networks

Published:2022-10-16 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Conference on Image Processing (ICIP)
language:
Short-container-title:

Author:

Alkhalifah Tariq¹,Huang Xinquan¹

Affiliation:

1. King Abdullah University of Science and Technology,Physical Sciences and Engineering Division

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9897158/9897159/09897269.pdf?arnumber=9897269

Reference25 articles.

1. Mapping full seismic waveforms to vertical velocity profiles by deep learning

2. Deep Theory of Functional Connections: A New Method for Estimating the Solutions of Partial Differential Equations

3. Single reference frequency loss for multi-frequency wavefield representation using physics-informed neural networks;huang;NeurIPS 2021 AI for Science Workshop,2021

Cited by 5 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Physics-informed neural wavefields with Gabor basis functions;Neural Networks;2024-07

2. Practical Aspects of Physics-Informed Neural Networks Applied to Solve Frequency-Domain Acoustic Wave Forward Problem;Seismological Research Letters;2024-01-09

3. Overcoming the Spectral Bias Problem of Physics-Informed Neural Networks in Solving the Frequency-Domain Acoustic Wave Equation;IEEE Transactions on Geoscience and Remote Sensing;2024

4. Physics-constrained neural networks for half-space seismic wave modeling;Computers & Geosciences;2023-12

5. Rapid surrogate modeling of magnetotelluric in the frequency domain using physics-driven deep neural networks;Computers & Geosciences;2023-07