prog_models v1.4

Release v1.4

• Data-Driven Models
  • Created new DataModel class as interface/superclass for all data-driven models. Data-driven models are interchangeable in use (e.g., simulation, use with prog_algs) with physics-based models. DataModels can be trained using data (.from_data), or an existing model (.from_model)
  • Introduced new LSTM State Transition DataModel. See lstm_model, full_lstm_model, and custom_model for examples of use
  • DMD model updated to new data-driven model interface. Can now be created from data as well as an existing model
  • Added ability to integrate training noise to data for DMD Model
• New Model: Single-Phase DC Motor
• Added the ability to select integration method when simulation. Current options are Euler and RK4
• New feature allowing serialization of model parameters as JSON. See serialization example
• Added automatic step size feature in simulation. When enabled, step size will adapt to meet the exact save_pts and save_freq. Step size range can also be bounded
• New Example Model: Simple Paris' Law
• Added ability to set bounds when estimating parameters (See PrognosticsModel.estimate_params())
• Initialize method is now optional
• Various bug fixes and performance improvements

Acknowledgements

Thank you to our intern Henry Lembo (@hlembo) for his contributions to this release.

This release includes contributions from NASA's Autonomous Spacecraft Operations (ASO), Data and Reasoning Fabric (DRF), System Wide Safety (SWS), and Transformative Tools & Technologies (TTT) projects. Thank you for your support!