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A physics-informed neural networks modeling with coupled fluid flow and heat transfer – Revisit of natural convection in cavity
Zahra Hashemi
, Maysam Gholampour
, Ming Chang Wu
, Ting Ya Liu
, Chuan Yi Liang
,
Chi Chuan Wang
*
*
此作品的通信作者
機械工程學系
研究成果
:
Article
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同行評審
25
引文 斯高帕斯（Scopus）
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Keyphrases
Accuracy Analysis
16%
Activation Function
16%
Benchmark Problems
16%
Boundary Loss
16%
Collocation Points
33%
Complex Interactions
16%
Computational Cost
33%
Confined Cavity
16%
Continuous Weights
16%
Convection in Cavities
100%
Convergence Analysis
16%
Convergence Behavior
16%
Coupled Fluid Flow
100%
Effort Cost
16%
Experimental Training
16%
Extra Effort
16%
Flow Transferring
100%
Fluid Flow
16%
Governing Equation
33%
Gradient Variation
16%
Heat Transfer
100%
Heat Transfer Problem
33%
High Rayleigh number
16%
High-resolution
16%
Hyperparameters
16%
Loss Function
50%
Meshless Approach
16%
Natural Convection
100%
Network Activation
16%
Network Architecture
16%
Network Size
16%
Neural Network
16%
Neural Network Method
16%
Neural Network Model
100%
Novel Technique
16%
Optimization Problem
16%
Partial Differential Equations
16%
Performance Analysis
16%
Physics-based
33%
Physics-informed Neural Networks
100%
Prediction Accuracy
33%
Problem-based
16%
Random number Generation
16%
Sampling numbers
16%
Sharp Discontinuity
16%
Simulation Runs
16%
Simulation Training
16%
Training Data
33%
Training Procedure
16%
Transfer Scenario
16%
Weighting Scheme
16%
Engineering
Activation Function
16%
Benchmark Problem
16%
Collocation Point
33%
Computational Cost
33%
Convergence Performance
16%
Coupled Fluid Flow
100%
Fluid Flow
16%
High Resolution
16%
Loss Function
50%
Natural Convection
100%
Network Model
33%
Optimisation Problem
16%
Partial Differential Equation
16%
Physics Informed Neural Network
100%
Rayleigh Number
16%
Chemical Engineering
Fluid Flow
100%
Natural Convection
100%
Neural Network
100%
Material Science
Fluid Flow
100%
Natural Convection
100%