Computational creativity via assisted variational synthesis of mechanisms using deep generative models

Keyphrases

• Mechanism Synthesis 100%
• Variational 100%
• Computational Creativity 100%
• Deep Generative Models 100%
• Computational Methods 66%
• Probability Distribution 66%
• Coupler 66%
• Kinematic Synthesis 66%
• User Input 66%
• Precision Position 66%
• Modeling Techniques 33%
• Deep Learning Architectures 33%
• Machine Learning 33%
• Highly Nonlinear 33%
• Deep Learning 33%
• Design Process 33%
• Conditional Distribution 33%
• Iterative Process 33%
• Motion Generation 33%
• Mechanism Design 33%
• Design Solutions 33%
• Conditional Variational Autoencoder 33%
• Computational Algorithm 33%
• Variational Autoencoder 33%
• Small Perturbation 33%
• Single Position 33%
• Input Samples 33%
• High-level Control 33%
• End-to-end Architecture 33%
• Kinematic Method 33%
• Computational Kinematics 33%
• End-to-end Deep Learning 33%
• Theoretical Kinematics 33%
• Trajectory Distribution 33%
• Deep Generative Modeling 33%
• Kinematic Linkage 33%

Engineering

• Generative Model 100%
• Salient Feature 100%
• Deep Learning Method 50%
• Deep Neural Network 50%
• Learning System 50%
• Mechanism Design 50%
• Design Process 50%
• Level Control 50%
• Design Solution 50%
• Neural Network Architecture 50%
• Iterative Process 50%
• Conditional Distribution 50%
• Input Sample 50%

Computer Science

• Computational Method 100%
• Deep Generative Model 100%
• Deep Learning Method 50%
• Machine Learning 50%
• Learning System 50%
• Missing Information 50%
• Deep Neural Network 50%
• Neural Network Architecture 50%
• Mechanism Design 50%
• Design Solution 50%
• Iterative Process 50%
• Conditional Distribution 50%