Uncovering Drone Intentions using Control Physics Informed Machine Learning: data
dc.contributor.author | Perrusquia Guzman, Adolfo | |
dc.contributor.author | Wei, Zhuangkun | |
dc.contributor.author | Guo, Weisi | |
dc.contributor.author | Fraser, Benjamin | |
dc.date.accessioned | 2024-06-03T06:46:27Z | |
dc.date.available | 2024-06-03T06:46:27Z | |
dc.date.issued | 2024-03-04 10:36 | |
dc.description.abstract | This repository provides the data and code of the paper "Uncovering Drone Intentions using Control Physics Informed Machine Learning" | |
dc.description.sponsorship | UKRI Trustworthy Autonomous Systems Node in Security | |
dc.identifier.citation | Perrusquia Guzman, Adolfo; Wei, Zhuangkun; Guo, Weisi; Fraser, Benjamin (2024). Uncovering Drone Intentions using Control Physics Informed Machine Learning: data. Cranfield Online Research Data (CORD). Software. https://doi.org/10.17862/cranfield.rd.25204409 | |
dc.identifier.doi | 10.17862/cranfield.rd.25204409 | |
dc.identifier.uri | https://dspace.lib.cranfield.ac.uk/handle/1826/21799 | |
dc.publisher | Cranfield University | |
dc.relation.supplements | https://github.com/CKPerrusquia/CPhy-ML' | |
dc.relation.supplements | 'https://doi.org/10.1038/s44172-024-00179-3' | |
dc.rights | Apache 2.0 | |
dc.rights.uri | https://www.apache.org/licenses/LICENSE-2.0.html | |
dc.subject | drone intentions' | |
dc.subject | 'physics informed' | |
dc.subject | 'reward function' | |
dc.subject | 'deep learning' | |
dc.subject | 'machine learning' | |
dc.title | Uncovering Drone Intentions using Control Physics Informed Machine Learning: data | |
dc.type | Software |
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