Physics-Informed Models
Combine machine learning with fundamental physics principles for better extrapolation
Gradient Boosting
High-performance ensemble models optimized for accuracy and speed
Ensemble Methods
Combine multiple models for improved accuracy and uncertainty quantification
Model Training Configuration
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Training History
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Training Guidelines & Best Practices
Model Selection
- • Gradient Boosting: Best for high accuracy with sufficient training data
- • Physics-Informed: Better extrapolation with limited data
- • Ensemble: Combines both approaches for optimal performance
- • Start with Gradient Boosting for most applications
Training Data Requirements
- • Minimum 1,000 validated samples recommended
- • Ensure balanced representation across component types
- • Include process variations and temperature effects
- • Validate data quality before training
Performance Targets
- • Insertion Loss: ±0.1 dB accuracy on 80% of predictions
- • Bandwidth: ±0.5 GHz accuracy on 75% of predictions
- • Crosstalk: ±1.0 dB accuracy on 70% of predictions
- • Training time should be under 5 minutes
Troubleshooting
- • Low accuracy: Increase training data or check data quality
- • High training time: Reduce model complexity or data size
- • Poor generalization: Add more diverse training samples
- • Overfitting: Use ensemble methods or regularization