Continual Learning Models in Aviation Systems
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Abstract
This paper explores the vital role of continual lifelong learning in advancing machine learning applications within the aviation industry. Through case studies on predictive maintenance and adaptive flight routing, we demonstrate how human-inspired continual learning enables AI systems to adapt incrementally to evolving conditions while preserving critical prior knowledge. This approach addresses fundamental challenges in dynamic, safety-critical aviation environments, promising improved adaptability, safety, and operational efficiency. The discussion highlights benefits, challenges, current progress, and future directions toward building resilient, intelligent aviation AI systems.
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References
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