Simulating Proto Planetary Disks with Deep Networks

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dc.contributor.authorShen, Haotian
dc.contributor.supervisorYi, Kwang Moo
dc.contributor.supervisorGanti, Sudhakar
dc.date.accessioned2019-08-29T16:16:17Z
dc.date.available2019-08-29T16:16:17Z
dc.date.copyright2019en_US
dc.date.issued2019-08-29
dc.degree.departmentDepartment of Computer Science
dc.degree.levelMaster of Science M.Sc.en_US
dc.description.abstractProto planetary disks provide hints to how planets are formed. To understand them, it is necessary to run multiple simulations with various hyperparameters through trial-and-error. This procedure is typically time consuming as each simulation is expensive. In this project, we aim to solve this problem by learning a deep network that interpolate and extrapolate, given two simulation outcomes. We then iteratively apply this network to extrapolate simulations. Specifically, as proto planetary disks are circular, we make use of the log-polar representation of these disks and apply a circular 1D convolution on it. We empirically motivate our design choices via ablation study. Our experimental results show encouraging outcomes on approximating proto planetary simulations through extrapolation.en_US
dc.description.scholarlevelGraduateen_US
dc.identifier.urihttp://hdl.handle.net/1828/11078
dc.language.isoen_USen_US
dc.rightsAvailable to the World Wide Weben_US
dc.subjectdeep networken_US
dc.subjectsimulationen_US
dc.subjectproto planetary disken_US
dc.subject1d convolutionen_US
dc.subjectcyclical paddingen_US
dc.subjectextrapolationen_US
dc.subjectinterpolationen_US
dc.subjectiterative extrapolationen_US
dc.titleSimulating Proto Planetary Disks with Deep Networksen_US
dc.typeprojecten_US

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