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School of Physics

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Author: Graham, Michael × End date: 2023 × Start date: 2020 × search.filters.applied.f.isOrgUnitOfPublicationTitle: College of Sciences ×

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    Deep Reinforcement Learning Using Data-Driven Reduced-Order Models Discovers and Stabilizes Low Dissipation Equilibria
    ( 2021-10-27) Graham, Michael ; Zeng, Kevin
    Mike Graham Overview: Our overall aim is to combine ideas from dynamical systems theory and machine learning to develop and apply reduced-order models of flow processes with complex chaotic dynamics. A particular aim is a minimal description of dynamics on manifolds of dimension much less than the nominal state dimension and use of these models to develop effective control strategies for reducing energy dissipation.
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    Modeling chaotic spatiotemporal dynamics with a minimal representation using Neural ODEs
    ( 2021-10-20) Graham, Michael ; Linot, Alec
    Mike Graham Overview: Our overall aim is to combine ideas from dynamical systems theory and machine learning to develop and apply reduced-order models of flow processes with complex chaotic dynamics. A particular aim is a minimal description of dynamics on manifolds of dimension much less than the nominal state dimension and use of these models to develop effective control strategies for reducing energy dissipation.