Highlights and New Additions
- Bayesian 3D U-Net model integrating Bayesian layers and attention blocks for uncertainty quantification and superior decoder performance leveraging the where to look capability with multi-task capabilities to estimate bot real-valued(regression) and categorical(classification) based values. The Decoder is used to obtain real-valued segmentation maps
- Deep Reinforcement Learning using deep deterministic policy gradient (DDPG) and a custom made multi physics manufacturing environment to build agents to correct manufacturing systems
- Closed Loop Sampling for faster model training and convergence using epistemic uncertainty of the Bayesian CNN models
- Matlab Python Integration to enable low latency connection between multi-physics manufacturing environments (Matlab) and TensorFlow based DDPG agents
- Multi-Physics Manufacturing System Simulations to generate custom datasets for various fault scenarios using Variation Response Method (VRM) kernel
- Uncertainty guided continual learning to enable life long/incremental training for multiple case studies
- Exploratory notebooks for various case studies
- 3D Gradient-weighted Class Activation Maps for interpretability of deep learning models
- Datasets for Industrial multi-station case studies for training and benchmarking deep learning models