研究论文
Emerging applications of physics-informed and physics-guided machine learning in geoenergy science: A review
Shadfar Davoodi, David A. Wood, Mohammed Al-Shargabi, Vladimir Vanovskiy, Valeriy S. Rukavishnikov, Evgeny Vladimirovich Burnaev
Skolkovo Institute of Science and Technology Tomsk Polytechnic University AIRI - Artificial Intelligence Research Institute
来源Communications in Nonlinear Science and Numerical Simulation
年份2025
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学术脉络
学科主题
物理Model Reduction and Neural Networks
Machine Learning in Materials Science · Reservoir Engineering and Simulation Methods
参考文献 82
Machine learning of linear differential equations using Gaussian processes
被引 609Maziar Raissi, Paris Perdikaris, George Em Karniadakis · Journal of Computational Physics · 2017
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
被引 19,913Maziar Raissi, Paris Perdikaris, George Em Karniadakis · Journal of Computational Physics · 2018
Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data
被引 1,067Luning Sun, Han Gao, Shaowu Pan · Computer Methods in Applied Mechanics and Engineering · 2019
此处列出前 3 条
引用本文 4
Stabilized hidden physics models via hybrid kernels for the data-driven discovery of nonlinear PDEs
被引 0Meysam Cheraghi, Mohsen Esmaeilbeigi, Ebrahim Nazari · Communications in Nonlinear Science and Numerical Simulation · 2026
Physics-informed feature-based machine learning and probabilistic process control of hydrodynamic instabilities in confined multiphase microflows: A unified framework for soft sensing, regime classification, and closed-loop microreactor automation
被引 0Eric Kwame Owusu, Yue Wang, N Liu · Computers & Chemical Engineering · 2026
Physics-informed machine learning in petroleum engineering: advances, applications, and future directions
被引 0Shadfar Davoodi, David A. Wood, Mohammed Al-Shargabi · Communications in Nonlinear Science and Numerical Simulation · 2026
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