- Implemented LSTM model with enhanced training features including mixed precision, early stopping, and validation via DataLoader.
- Introduced a new 1D Temporal Convolutional Network (TCN) model for predicting hysteretic curves.
- Added functionality for creating sliding windows from the dataset for both training and testing.
- Integrated scaling for features and targets using StandardScaler.
- Enhanced data handling with explicit cleanup and memory management for GPU.
- Updated main function to include model training and prediction for both LSTM and TCN.
- Saved predictions to CSV files for further analysis.

- Introduced `plot_data_hysteretic_curves.py` for visualizing envelope curves, energy degradation, and stiffness degradation from cycle-level data.
- Implemented `predict_hysteretic_curves.py` to create a sliding window dataset for training an LSTM model to predict hysteretic force-displacement curves.
- Enhanced the manuscript with updated references to force-displacement relationships and clarified the role of LSTM models in the optimization framework.
- Updated manuscript files to reflect changes in the document structure and content.

feat: Generate and store new machine learning and RBF models, cross-validation results, and optimization reports for width optimization, and remove outdated model and report files.