Michael D. Graham

Publication Activity (10 Years)

Years Active: 2012-2024
Publications (10 Years): 17

Top Topics

Dynamical Systems

Ordinary Differential Equations

Top Venues

J. Comput. Phys.

Nat. Mac. Intell.

Publications

Andrew J. Fox, Michael D. Graham
Data-driven low-dimensional model of a sedimenting flexible fiber. CoRR (2024)
Alec J. Linot, Kevin Zeng, Michael D. Graham
Turbulence control in plane Couette flow using low-dimensional neural ODE-based models and deep reinforcement learning. CoRR (2023)
Kevin Zeng, Michael D. Graham
Autoencoders for discovering manifold dimension and coordinates in data from complex dynamical systems. CoRR (2023)
C. Ricardo Constante-Amores, Alec J. Linot, Michael D. Graham
Enhancing Predictive Capabilities in Data-Driven Dynamical Modeling with Automatic Differentiation: Koopman and Neural ODE Approaches. CoRR (2023)
Carlos E. Pérez De Jesús, Alec J. Linot, Michael D. Graham
Building symmetries into data-driven manifold dynamics models for complex flows. CoRR (2023)
Alec J. Linot, Joshua W. Burby, Qi Tang, Prasanna Balaprakash, Michael D. Graham, Romit Maulik
Stabilized neural ordinary differential equations for long-time forecasting of dynamical systems. J. Comput. Phys. 474 (2023)
Alec J. Linot, Michael D. Graham
Dynamics of a data-driven low-dimensional model of turbulent minimal Couette flow. CoRR (2023)
Alec J. Linot, Joshua W. Burby, Qi Tang, Prasanna Balaprakash, Michael D. Graham, Romit Maulik
Stabilized Neural Ordinary Differential Equations for Long-Time Forecasting of Dynamical Systems. CoRR (2022)
Charles D. Young, Michael D. Graham
Deep learning delay coordinate dynamics for chaotic attractors from partial observable data. CoRR (2022)
Daniel Floryan, Michael D. Graham
Data-driven discovery of intrinsic dynamics. Nat. Mac. Intell. 4 (12) (2022)
Kevin Zeng, Alec J. Linot, Michael D. Graham
Data-driven control of spatiotemporal chaos with reduced-order neural ODE-based models and reinforcement learning. CoRR (2022)
Carlos E. Pérez De Jesús, Michael D. Graham
Data-driven low-dimensional dynamic model of Kolmogorov flow. CoRR (2022)
Alec J. Linot, Michael D. Graham
Data-Driven Reduced-Order Modeling of Spatiotemporal Chaos with Neural Ordinary Differential Equations. CoRR (2021)
Daniel Floryan, Michael D. Graham
Charts and atlases for nonlinear data-driven models of dynamics on manifolds. CoRR (2021)
Kevin Zeng, Michael D. Graham
Symmetry reduction for deep reinforcement learning active control of chaotic spatiotemporal dynamics. CoRR (2021)
Rishav Agrawal, Henry C.-H. Ng, Ethan A. Davis, Jae Sung Park, Michael D. Graham, David J. C. Dennis, Robert J. Poole
Low- and High-Drag Intermittencies in Turbulent Channel Flows. Entropy 22 (10) (2020)
Alec J. Linot, Michael D. Graham
Deep learning to discover and predict dynamics on an inertial manifold. CoRR (2020)
Amit Kumar, Michael D. Graham
Accelerated boundary integral method for multiphase flow in non-periodic geometries. J. Comput. Phys. 231 (20) (2012)