Neural network training strategies for hysteresis modeling, focusing on techniques to improve model performance and generalization for magnetic field prediction tasks.
Training Methodologies
Progressive Learning
- Curriculum Learning - Learning simple to complex patterns
- Curriculum Learning Stage 1 through Curriculum Learning Stage 5 - Staged complexity introduction
- Transfer Learning and Fine-tuning - Adapting pre-trained models
Optimization Strategies
- Choice of optimizer - Adam, AdamW, Ranger, Lion comparisons
- OneCycleLR scheduling for learning rate management
- Weight decay and dropout tuning
Data Strategies
Preprocessing
- Time Series Compression - Reducing computational load
- Adaptive downsampling - Preserving signal fidelity
- Data Pre-Processing - Pipeline preparation
Data Quality
- Drift-reduction on MBI data - Signal stability
- MBI data noise analysis - Quality assessment
- Low amplitude noise filter - Signal cleaning
Model Architectures
Transformer-Based
- Temporal Fusion Transformer - Primary architecture
- PETE - Physics-enhanced approach
- Transformer Encoder - Base experiments
Recurrent Networks
- LSTM variants for sequence modeling
- PhyLSTM - Physics-informed recurrent networks
- AttentionLSTM - Attention-enhanced LSTM
Related Concepts
- Hysteresis Compensation Studies - Application domain
- Model Zoo - Trained model registry
- Foundation model for hysteresis modeling - Base model development