Fatigue reliability analysis of aeroengine blade-disc systems using physics-informed ensemble learning

Keyphrases

• Fatigue Reliability 100%
• Physics-informed 100%
• Aero-engine Blade 100%
• Ensemble Learning 100%
• Disk System 100%
• Modeling Method 40%
• Reliability Analysis 40%
• Physical Characteristics 40%
• Component Reliability Analysis 40%
• Structural Integrity 20%
• Computational Efficiency 20%
• Computational Accuracy 20%
• Multi-material 20%
• Low Computational 20%
• System Reliability 20%
• Single Component 20%
• Computing Accuracy 20%
• Least Squares Support Vector Regression (LSSVR) 20%
• Regression Neural Network 20%
• Aero-engine 20%
• Neural Network Ensemble 20%
• Component Reliability 20%
• Copula Function 20%
• Physics-informed Machine Learning 20%
• Physics-informed Neural Networks 20%
• Ensemble Learning Method 20%
• Computing Efficiency 20%
• High Pressure Turbine Blade 20%
• Ensemble Learning Model 20%
• Integral Modeling 20%
• Direct Monte Carlo 20%
• Constitutive Response 20%
• Failure Correlation 20%
• Direct Integral 20%
• Learning-enabled Systems 20%
• Physics-based Modeling 20%

Engineering

• Reliability Analysis 100%
• Fatigue Reliability 100%
• Aeroengine 100%
• Structural Integrity 14%
• Computational Efficiency 14%
• System Reliability 14%
• Single Component 14%
• High-Pressure Turbine 14%
• Learning System 14%
• Physics Informed Neural Network 14%
• Support Vector Machine 14%