Improving the Digital Aviation Readiness Technology Engine (DARTE) with Temporal Pattern Attention Mechanisms and Hyper-Deep Ensembles
Abstract
The Digital Aviation Readiness Technology Engine (DARTE) provides unprecedented predictive readiness capabilities for the Naval FA-18 fleet. DARTE focuses on discovering actionable insights in relation to predicting two key readiness metrics: the number of mission capable (MC) aircraft and flight hours. Recent DARTE efforts have focused on improvements including the adoption of cutting edge artificial intelligence (AI) and deep learning techniques such as temporal pattern attention mechanism-enhanced long short-term memory (LSTMA) networks, hyper-deep ensembles for enhanced performance, and improved uncertainty estimation and robustness. Hyper-deep ensembles and attention mechanisms have been shown to provide state-of-the art results in industry and academia. Furthermore, their improved uncertainty estimation provides decision makers with an increased level of confidence that allows for better, smarter decisions.
Document Details
- Document Type
- Technical Report
- Publication Date
- Feb 01, 2022
- Accession Number
- AD1158586
Entities
People
- Andrew B. Sabater
- Benjamin A. Michlin
- Dean Lee
- Gary R. Williams
- Jamal T. Rorie
- Josh A. Duclos
Organizations
- Naval Information Warfare Center Pacific