Physics-informed multi-grid neural operator: theory and an application to porous flow simulation
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Publication:6648363
DOI10.1016/J.JCP.2024.113438MaRDI QIDQ6648363
Suihong Song, Dongxiao Zhang, Tapan Mukerji
Publication date: 4 December 2024
Published in: Journal of Computational Physics (Search for Journal in Brave)
inversionphysics-informed neural networksmulti-grid neural operatorPDE solversporous flow simulations
Artificial intelligence (68Txx) Numerical methods for partial differential equations, initial value and time-dependent initial-boundary value problems (65Mxx) Geophysics (86Axx)
Cites Work
- Deep-learning-based surrogate flow modeling and geological parameterization for data assimilation in 3D subsurface flow
- Geological facies modeling based on progressive growing of generative adversarial networks (GANs)
- GANSim: conditional facies simulation using an improved progressive growing of generative adversarial networks (GANs)
- Physics-informed PointNet: a deep learning solver for steady-state incompressible flows and thermal fields on multiple sets of irregular geometries
- Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data
- Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks
- Theory-guided auto-encoder for surrogate construction and inverse modeling
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Prediction of aerodynamic flow fields using convolutional neural networks
- Multi-stage Neural Networks: Function Approximator of Machine Precision
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