Deep Reinforcement Learning Based Systems for Safety Critical Applications in Aerospace Article Swipe
Recent advancements in artificial intelligence (AI) applications within aerospace have demonstrated substantial growth, particularly in the context of control systems. As High Performance Computing (HPC) platforms continue to evolve, they are expected to replace current flight control or engine control computers, enabling increased computational capabilities. This shift will allow real-time AI applications, such as image processing and defect detection, to be seamlessly integrated into monitoring systems, providing real-time awareness and enhanced fault detection and accommodation. Furthermore, AI's potential in aerospace extends to control systems, where its application can range from full autonomy to enhancing human control through assistive features. AI, particularly deep reinforcement learning (DRL), can offer significant improvements in control systems, whether for autonomous operation or as an augmentative tool.
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Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2412.16489
- https://arxiv.org/pdf/2412.16489
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405765931
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4405765931Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2412.16489Digital Object Identifier
- Title
-
Deep Reinforcement Learning Based Systems for Safety Critical Applications in AerospaceWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-12-21Full publication date if available
- Authors
-
Abedin SherifiList of authors in order
- Landing page
-
https://arxiv.org/abs/2412.16489Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2412.16489Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2412.16489Direct OA link when available
- Concepts
-
Aerospace, Reinforcement learning, Reinforcement, Life-critical system, Computer science, Engineering, Artificial intelligence, Aerospace engineering, Software, Structural engineering, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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