Effective design space exploration of gradient nanostructured materials using active learning based surrogate models

Keyphrases

• Active Learning 100%
• Adaptive Design 40%
• Adaptive Sequential Sampling 20%
• Design Framework 20%
• Design Optimization 20%
• Design Space 20%
• Design Space Exploration 100%
• Effective Design 100%
• Enhanced Strain 20%
• Fatigue Resistance 20%
• Gaussian Process 20%
• Gradient Nanostructure 100%
• Gradient Nanostructured Metals 100%
• Gradient Structure 60%
• Gradient Thickness 20%
• Grain Size Gradient 20%
• High Strength 20%
• Learning-based 100%
• Material Combination 20%
• Material Properties 20%
• Maximum Strength 20%
• Metal Cans 20%
• Modeling Uncertainty 20%
• Nanostructured Materials 100%
• Physics-based Simulation 40%
• Process-based 20%
• Rational Design 20%
• Restricted Sampling 20%
• Sampling Data 20%
• Strain Hardening 20%
• Structure-property Relationships 40%
• Surrogate Model 100%
• Surrogate Modeling Technique 20%
• Tensile Ductility 20%
• Thickness Gradient 20%
• Tunable Microstructures 20%
• Twin Thickness 20%
• Uncertainty Optimization 20%
• Uncertainty Quantification 20%
• Wear Resistance 20%

Engineering

• Adaptive Design 100%
• Design Space 100%
• Enhanced Strain 50%
• Gaussians 50%
• Good Strength 50%
• Microstructure 50%
• Nanomaterial 100%
• Optimised Design 50%
• Property Relationship 100%
• Strain Hardening 50%
• Surrogate Model 100%
• Tensiles 50%
• Uncertainty Quantification 50%
• Wear Resistance 50%

Material Science

• Fatigue of Materials 33%
• Grain Size 33%
• Materials Property 33%
• Mechanical Strength 100%
• Nanocrystalline Material 100%
• Surrogate Modeling 100%
• Wear Resistance 33%
• Work Hardening 33%