Physics-Informed Neural Networks for Scientific Computing (Q166495): Difference between revisions

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Deep Learning
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Partial Differential Equations
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Model Reduction
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Fluid Dynamics
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Dynamic Mode Decomposition
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Nonlinear Systems
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Machine Learning
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Data-Driven Modeling
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Numerical Computing
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Inverse Problems
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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
Using AI to solve complex math problems in physics and engineering.
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Property / same as: https://openalex.org/T11206 / rank
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Property / uses: machine learning / rank
 
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Property / uses: deep learning / rank
 
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Property / OpenAlex ID: T11206 / rank
 
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Property / addresses subject: deep learning / rank
 
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Property / addresses subject: machine learning / rank
 
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Latest revision as of 20:19, 12 September 2024

Using AI to solve complex math problems in physics and engineering.
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Physics-Informed Neural Networks for Scientific Computing
Using AI to solve complex math problems in physics and engineering.

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