A critical review of physics-informed machine learning applications in subsurface energy systems-Reference-Cited by-同舟云学术

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A critical review of physics-informed machine learning applications in subsurface energy systems

Published:2024-08 Issue: Volume:239 Page:212938
ISSN:2949-8910
Container-title:Geoenergy Science and Engineering
language:en
Short-container-title:Geoenergy Science and Engineering

Author:

Latrach Abdeldjalil^ORCID,Malki Mohamed L.^ORCID,Morales Misael^ORCID,Mehana Mohamed^ORCID,Rabiei Minou^ORCID

Publisher

Elsevier BV

Reference194 articles.

1. Simulation and prediction of countercurrent spontaneous imbibition at early and late times using physics-informed neural networks;Abbasi,2023

2. Prediction of inflow performance relationship of a gas field using artificial intelligence techniques;AL-Dogail,2018

3. Is it time to swish? Comparing activation functions in solving the Helmholtz equation using physics-informed neural networks;Al-Safwan,2021

4. Extended Physics-Informed Neural Networks for Solving Fluid Flow Problems in Highly Heterogeneous Media;Alhubail,2022

5. High-dimensional wavefield solutions based on neural network functions;Alkhalifah,2021

Cited by 2 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. Understanding Physics-Informed Neural Networks: Techniques, Applications, Trends, and Challenges;AI;2024-08-29

2. Stochastic pix2vid: A new spatiotemporal deep learning method for image-to-video synthesis in geologic CO$$_2$$ storage prediction;Computational Geosciences;2024-06-20

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