This webinar will present several emerging applications of artificial intelligence and machine learning (ML) in naval energy autonomy and digital transformation and summarize relevant research and development efforts carried out by the University of Dayton Research Institute (UDRI) in a contract with the Naval Facility Engineering and Expeditionary Warfare Center.
The unique solution recently developed at UDRI is a unified data-driven, predictive modeling and control method for energy systems and a physics-guided Bayesian neural learning framework with probabilistic weights for learnable parameters in the networks. The technologies derived from this can be transitioned to the U.S. naval installation energy infrastructure (such as naval base facilities and microgrids), shipboard power systems, naval aviation operational energy systems, naval logistics, and naval enterprise systems.
This webinar presentation will answer the following topics:
- Why does the U.S. Navy want to use ML and predictive analytics for energy systems?
- What are the features of an ML analytics solution?
- Based on ML and model predictive control, how does the control system function in an autonomous energy system?
- What are the applications of the ML methods for naval energy systems?
- What are the anticipated benefits and recommendations for future development?