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HNN is used to learn potential Hamiltonian physical quantities, so as to understand more chaotic system dynamics information to realize state cognition. Velocity\u2013Verlet integrator predicts the motion state at any moment according to the Hamiltonian learned by HNN at the current moment. The motion state and the specified time are used as the input of CVAE decoder to generate the target prediction image from the potential motion space. Experimental results show that CHGN can accurately predict target trajectories over a long period of time based on incomplete short-term image sequences, and has better performance with minimum mean square error(MSE) on three physical system datasets than existing deep learning methods.<\/jats:p>","DOI":"10.1007\/s40747-022-00769-8","type":"journal-article","created":{"date-parts":[[2022,5,25]],"date-time":"2022-05-25T08:03:38Z","timestamp":1653465818000},"page":"5439-5448","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Trajectory prediction based on conditional Hamiltonian generative network for incomplete observation image sequences"],"prefix":"10.1007","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6329-3803","authenticated-orcid":false,"given":"Kui","family":"Qian","sequence":"first","affiliation":[]},{"given":"Lei","family":"Tian","sequence":"additional","affiliation":[]},{"given":"Aiguo","family":"Song","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,5,25]]},"reference":[{"key":"769_CR1","doi-asserted-by":"crossref","unstructured":"Deo N, Trivedi MM (2018) Convolutional social pooling for vehicle trajectory prediction. 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