Uncovering Drone Intentions using Control Physics Informed Machine Learning: data

dc.contributor.authorPerrusquia Guzman, Adolfo
dc.contributor.authorWei, Zhuangkun
dc.contributor.authorGuo, Weisi
dc.contributor.authorFraser, Benjamin
dc.date.accessioned2024-06-03T06:46:27Z
dc.date.available2024-06-03T06:46:27Z
dc.date.issued2024-03-04 10:36
dc.description.abstractThis repository provides the data and code of the paper "Uncovering Drone Intentions using Control Physics Informed Machine Learning"
dc.description.sponsorshipUKRI Trustworthy Autonomous Systems Node in Security
dc.identifier.citationPerrusquia 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.doi10.17862/cranfield.rd.25204409
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/21799
dc.publisherCranfield University
dc.relation.supplementshttps://github.com/CKPerrusquia/CPhy-ML'
dc.relation.supplements'https://doi.org/10.1038/s44172-024-00179-3'
dc.rightsApache 2.0
dc.rights.urihttps://www.apache.org/licenses/LICENSE-2.0.html
dc.subjectdrone intentions'
dc.subject'physics informed'
dc.subject'reward function'
dc.subject'deep learning'
dc.subject'machine learning'
dc.titleUncovering Drone Intentions using Control Physics Informed Machine Learning: data
dc.typeSoftware

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