A PRELIMINARY STUDY OF LEARNING A WAVE ENERGY CONVERTER SYSTEM USING PHYSICS-INFORMED NEURAL NETWORK METHOD

Keyphrases

• Neural Network Method 100%
• Converter System 100%
• Wave Energy Converter 100%
• Physics-informed Neural Networks 100%
• Model Accuracy 16%
• Governing Equation 16%
• Computational Efficiency 16%
• Neural Network Model 16%
• Equations of Motion 16%
• Time-domain Simulation 16%
• Time Series Segmentation 16%
• Segmentation Approach 16%
• Wave Conditions 16%
• Differential Equations 16%
• Physical Problems 16%
• Numerical Benchmark 16%
• Machine Learning Workflow 16%
• Overall Modeling 16%
• Time-dependent Problems 16%
• Neural Network Simulation 16%
• Floating Spheres 16%
• Decay Test 16%

Engineering

• Power Converter 100%
• Wave Energy 100%
• Physics Informed Neural Network 100%
• Time Domain 16%
• Network Model 16%
• Computational Efficiency 16%
• Numerical Model 16%
• Physical Problem 16%
• Learning System 16%

Physics

• Physics 100%
• Neural Network 100%
• Direct Power Generator 100%
• Operators (Mathematics) 14%
• Numerical Model 14%
• Equation of Motion 14%
• Machine Learning 14%