Physics-Informed Neural Networks for Scientific Computing (Q166495): Difference between revisions
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Revision as of 18:32, 24 June 2024
This cluster of papers focuses on the development and application of physics-informed neural networks for scientific computing, particularly in the context of solving partial differential equations, model reduction, fluid dynamics, dynamic mode decom
- Deep Learning
- Partial Differential Equations
- Model Reduction
- Fluid Dynamics
- Dynamic Mode Decomposition
- Nonlinear Systems
- Data-Driven Modeling
- Numerical Computing
- Inverse Problems
| Language | Label | Description | Also known as |
|---|---|---|---|
| English | Physics-Informed Neural Networks for Scientific Computing |
This cluster of papers focuses on the development and application of physics-informed neural networks for scientific computing, particularly in the context of solving partial differential equations, model reduction, fluid dynamics, dynamic mode decom |
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