Continual Learning Models in Aviation Systems

Main Article Content

Nicki Barari
Ghazal Barari

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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Peer-Reviewed Articles

References

Barari, G., & Sanders, B. (2024, February). The effectiveness of virtual environments for increasing engagement in engineering technology courses. In the 2024 Conference for Industry and Education Collaboration (CIEC).

Barari, N., & Kim, E. (2021, November). Linking sparse coding dictionaries for representation learning. In 2021 International Conference on Rebooting Computing (ICRC) (pp. 84–87). IEEE. https://doi.org/10.1109/ICRC54067.2021.00024

Barari, N., Lian, X., & MacLellan, C. J. (2024). Avoiding catastrophic forgetting in visual classification using human concept formation. arXiv e-prints, arXiv-2402.

Barari, N., Lian, X., & MacLellan, C. J. (2024). Incremental concept formation over visual images without catastrophic forgetting. arXiv Preprint. https://arxiv.org/abs/2402.16933

Czigler, I., & Winkler, I. (Eds.). (2010). Unconscious memory representations in perception: Processes and mechanisms in the brain (Vol. 78). John Benjamins Publishing.

Fisher, D. (1996). Iterative optimization and simplification of hierarchical clusterings. Journal of Artificial Intelligence Research, 4, 147–178. https://doi.org/10.1613/jair.265

Fisher, D. H. (1987). Knowledge acquisition via incremental conceptual clustering. Machine Learning, 2, 139–172. https://doi.org/10.1007/BF00169894

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning (Vol. 1). MIT Press.

Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative adversarial nets. In Advances in Neural Information Processing Systems, 27. https://papers.nips.cc/paper_files/paper/2014/hash/5ca3e9b122f61f8f06494c97b1afccf3-Abstract.html

Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A., ... & Hadsell, R. (2017). Overcoming catastrophic forgetting in neural networks. Proceedings of the National Academy of Sciences, 114(13), 3521–3526. https://doi.org/10.1073/pnas.1611835114

Li, Z., & Hoiem, D. (2017). Learning without forgetting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(12), 2935–2947. https://doi.org/10.1109/TPAMI.2017.2773081

McCloskey, M., & Cohen, N. J. (1989). Catastrophic interference in connectionist networks: The sequential learning problem. In G. H. Bower (Ed.), Psychology of learning and motivation (Vol. 24, pp. 109–165). Academic Press. https://doi.org/10.1016/S0079-7421(08)60536-8

Olshausen, B. A., & Field, D. J. (1996). Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature, 381(6583), 607–609. https://doi.org/10.1038/381607a0

Parisi, G. I., Kemker, R., Part, J. L., Kanan, C., & Wermter, S. (2019). Continual lifelong learning with neural networks: A review. Neural Networks, 113, 54–71. https://doi.org/10.1016/j.neunet.2019.01.012

Rusu, A. A., Rabinowitz, N. C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., ... & Hadsell, R. (2016). Progressive neural networks. arXiv Preprint. https://arxiv.org/abs/1606.04671

Shin, H., Lee, J. K., Kim, J., & Kim, J. (2017). Continual learning with deep generative replay. In Advances in Neural Information Processing Systems, 30.

Wang, Z., Haarer, E. L., Barari, N., & MacLellan, C. J. (2025). Taxonomic Networks: A Representation for Neuro-Symbolic Pairing. arXiv preprint arXiv:2505.24601.