PhySR: physics-informed deep super-resolution for spatiotemporal data
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Publication:6054215
DOI10.1016/j.jcp.2023.112438zbMath1522.65186arXiv2208.01462OpenAlexW4386034616MaRDI QIDQ6054215
No author found.
Publication date: 27 September 2023
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2208.01462
partial differential equationssuper-resolutionphysics-informed learningscientific datahard-encoding scheme
Artificial neural networks and deep learning (68T07) Numerical methods for partial differential equations, initial value and time-dependent initial-boundary value problems (65M99)
Cites Work
- PhyCRNet: physics-informed convolutional-recurrent network for solving spatiotemporal PDEs
- Physics-informed graph neural Galerkin networks: a unified framework for solving PDE-governed forward and inverse problems
- PhyGeoNet: physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain
- Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data
- PDE-Net 2.0: learning PDEs from data with a numeric-symbolic hybrid deep network
- Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks
- Physics-informed multi-LSTM networks for metamodeling of nonlinear structures
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
- Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows
- Learning data-driven discretizations for partial differential equations
- Image Super-Resolution Via Sparse Representation
- Transformers for modeling physical systems
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