{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/dynode-neural-ordinary-differential-equations","title":"DyNODE: Neural Ordinary Differential Equations for Dynamics Modeling in Continuous Control","arxiv_id":"2009.04278","date":"2020-09-09","proceeding":null,"authors":["Victor M. Martinez Alvarez","Rareş Roşca","Cristian G. Fălcuţescu"],"abstract":"We present a novel approach (DyNODE) that captures the underlying dynamics of a system by incorporating control in a neural ordinary differential equation framework. 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