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ItemDeep Reinforcement Learning Using Data-Driven Reduced-Order Models Discovers and Stabilizes Low Dissipation Equilibria( 2021-10-27) Graham, Michael ; Zeng, KevinMike 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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ItemModeling chaotic spatiotemporal dynamics with a minimal representation using Neural ODEs( 2021-10-20) Graham, Michael ; Linot, AlecMike 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.