A machine learning methodology for inferring network S-parameters in the presence of variability

Keyphrases

• S-parameters 100%
• Machine Learning Techniques 100%
• Variational Autoencoder 66%
• Modeling Techniques 33%
• Vector Fitting 33%
• Post-processing Techniques 33%
• Physical Constraints 33%
• Probability Distribution 33%
• Training Data 33%
• Finite Order 33%
• Rational Functions 33%
• Microstrip 33%
• Manufacturing Variations 33%
• Multiport Network 33%
• Data Generating Process 33%
• Statistical Simulation 33%
• Strip Transmission Line 33%
• Generative Modeling 33%
• Generated Samples 33%
• Linear multiports 33%
• Generative Networks 33%

Engineering

• Scattering Parameters 100%
• Learning System 100%
• Autoencoder 66%
• Constrains 33%
• Processing Step 33%
• Electric Lines 33%
• Physical Constraint 33%
• Generated Sample 33%
• Micro Strip 33%

Material Science

• Variational Autoencoder 100%