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A shallow physics-informed neural network for solving partial differential equations on static and evolving surfaces
Wei Fan Hu
*
, Yi Jun Shih,
Te Sheng Lin
, Ming Chih Lai
*
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應用數學系
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引文 斯高帕斯（Scopus）
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Keyphrases
Partial Differential Equations
100%
Static Surface
100%
Physics-informed Neural Networks
100%
Evolving Surfaces
100%
Hidden Layer
75%
Numerical Results
25%
Surfactant
25%
Result Prediction
25%
Network Structure
25%
Level Set Function
25%
Performance Studies
25%
Loss Function
25%
Surface Diffusion
25%
Mean Curvature
25%
Surface Normal
25%
Diffusion Equation
25%
Shear Flow
25%
Deformable Surface
25%
Number of Neurons
25%
Differential Expression
25%
Topological Structure
25%
Homeomorphism
25%
Comparable Accuracy
25%
Differential Operator
25%
Normal Extension
25%
Differential Use
25%
Surface Differential Operators
25%
Advection-diffusion Equation
25%
Normal Curvature
25%
Mathematics
Neural Network
100%
Differential Operator
100%
Solving Partial Differential Equation
100%
Level Set
50%
Set Function
50%
Loss Function
50%
Mean Curvature
50%
Diffusion Equation
50%
Homeomorphism
50%
Topological Structure
50%
Normal (Extension)
50%
Differential Expression
50%
Normal Curvature
50%
Engineering
Hidden Layer
100%
Partial Differential Equation
100%
Physics Informed Neural Network
100%
Surfactant
33%
Level Set Function
33%
Loss Function
33%
Shear Flow
33%
Diffusion Equation
33%
Static Case
33%
Homeomorphism
33%
Computer Science
Physics-Informed Neural Network
100%
Partial Differential Equation
100%
Diffusion Equation
50%
Topological Structure
50%
Level Set Function
50%
Network Structure
50%
Material Science
Surface (Surface Science)
100%
Surface Active Agent
7%
Surface Diffusion
7%
Shear Flow
7%