Michael D. Graham

21 papers Journal 21

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Blending data and physics for reduced-order modeling of systems with spatiotemporal chaotic dynamics. CoRR Alex Guo, Michael D. Graham
2024	J	`jnl`	Autoencoders for discovering manifold dimension and coordinates in data from complex dynamical systems. Mach. Learn. Sci. Technol. Kevin Zeng, Carlos E. Pérez De Jesús, Andrew J. Fox, Michael D. Graham
2024	J	`jnl`	Data-driven low-dimensional model of a sedimenting flexible fiber. CoRR Andrew J. Fox, Michael D. Graham
2024	J	`jnl`	On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions. CoRR Jake Buzhardt, C. Ricardo Constante-Amores, Michael D. Graham
2023	J	`jnl`	Autoencoders for discovering manifold dimension and coordinates in data from complex dynamical systems. CoRR Kevin Zeng, Michael D. Graham
2023	J	`jnl`	Building symmetries into data-driven manifold dynamics models for complex flows. CoRR Carlos E. Pérez De Jesús, Alec J. Linot, Michael D. Graham
2023	J	`jnl`	Dynamics of a data-driven low-dimensional model of turbulent minimal Couette flow. CoRR Alec J. Linot, Michael D. Graham
2023	J	`jnl`	Enhancing Predictive Capabilities in Data-Driven Dynamical Modeling with Automatic Differentiation: Koopman and Neural ODE Approaches. CoRR C. Ricardo Constante-Amores, Alec J. Linot, Michael D. Graham
2023	J	`jnl`	Stabilized neural ordinary differential equations for long-time forecasting of dynamical systems. J. Comput. Phys. Alec J. Linot, Joshua W. Burby, Qi Tang, Prasanna Balaprakash, Michael D. Graham, Romit Maulik
2023	J	`jnl`	Turbulence control in plane Couette flow using low-dimensional neural ODE-based models and deep reinforcement learning. CoRR Alec J. Linot, Kevin Zeng, Michael D. Graham
2022	J	`jnl`	Data-driven control of spatiotemporal chaos with reduced-order neural ODE-based models and reinforcement learning. CoRR Kevin Zeng, Alec J. Linot, Michael D. Graham
2022	J	`jnl`	Data-driven discovery of intrinsic dynamics. Nat. Mac. Intell. Daniel Floryan, Michael D. Graham
2022	J	`jnl`	Data-driven low-dimensional dynamic model of Kolmogorov flow. CoRR Carlos E. Pérez De Jesús, Michael D. Graham
2022	J	`jnl`	Deep learning delay coordinate dynamics for chaotic attractors from partial observable data. CoRR Charles D. Young, Michael D. Graham
2022	J	`jnl`	Stabilized Neural Ordinary Differential Equations for Long-Time Forecasting of Dynamical Systems. CoRR Alec J. Linot, Joshua W. Burby, Qi Tang, Prasanna Balaprakash, Michael D. Graham, Romit Maulik
2021	J	`jnl`	Charts and atlases for nonlinear data-driven models of dynamics on manifolds. CoRR Daniel Floryan, Michael D. Graham
2021	J	`jnl`	Data-Driven Reduced-Order Modeling of Spatiotemporal Chaos with Neural Ordinary Differential Equations. CoRR Alec J. Linot, Michael D. Graham
2021	J	`jnl`	Symmetry reduction for deep reinforcement learning active control of chaotic spatiotemporal dynamics. CoRR Kevin Zeng, Michael D. Graham
2020	J	`jnl`	Deep learning to discover and predict dynamics on an inertial manifold. CoRR Alec J. Linot, Michael D. Graham
2020	J	`jnl`	Low- and High-Drag Intermittencies in Turbulent Channel Flows. Entropy Rishav Agrawal, Henry C.-H. Ng, Ethan A. Davis, Jae Sung Park, Michael D. Graham, David J. C. Dennis, Robert J. Poole
2012	J	`jnl`	Accelerated boundary integral method for multiphase flow in non-periodic geometries. J. Comput. Phys. Amit Kumar, Michael D. Graham