Michael D. Graham

According to our database, Michael D. Graham authored at least 16 papers between 2012 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Stabilized neural ordinary differential equations for long-time forecasting of dynamical systems.

Alec J. Linot, Joshua W. Burby, Qi Tang, Prasanna Balaprakash, Michael D. Graham, Romit Maulik

J. Comput. Phys., February, 2023

Building symmetries into data-driven manifold dynamics models for complex flows.

Carlos E. Pérez De Jesús, Alec J. Linot, Michael D. Graham

CoRR, 2023

Enhancing Predictive Capabilities in Data-Driven Dynamical Modeling with Automatic Differentiation: Koopman and Neural ODE Approaches.

C. Ricardo Constante-Amores, Alec J. Linot, Michael D. Graham

CoRR, 2023

Autoencoders for discovering manifold dimension and coordinates in data from complex dynamical systems.

Kevin Zeng, Michael D. Graham

CoRR, 2023

Turbulence control in plane Couette flow using low-dimensional neural ODE-based models and deep reinforcement learning.

Alec J. Linot, Kevin Zeng, Michael D. Graham

CoRR, 2023

Dynamics of a data-driven low-dimensional model of turbulent minimal Couette flow.

Alec J. Linot, Michael D. Graham

CoRR, 2023

2022

Data-driven discovery of intrinsic dynamics.

Daniel Floryan, Michael D. Graham

Nat. Mac. Intell., December, 2022

Deep learning delay coordinate dynamics for chaotic attractors from partial observable data.

Charles D. Young, Michael D. Graham

CoRR, 2022

Data-driven low-dimensional dynamic model of Kolmogorov flow.

Carlos E. Pérez De Jesús, Michael D. Graham

CoRR, 2022

Data-driven control of spatiotemporal chaos with reduced-order neural ODE-based models and reinforcement learning.

Kevin Zeng, Alec J. Linot, Michael D. Graham

CoRR, 2022

2021

Data-Driven Reduced-Order Modeling of Spatiotemporal Chaos with Neural Ordinary Differential Equations.

Alec J. Linot, Michael D. Graham

CoRR, 2021

Charts and atlases for nonlinear data-driven models of dynamics on manifolds.

Daniel Floryan, Michael D. Graham

CoRR, 2021

Symmetry reduction for deep reinforcement learning active control of chaotic spatiotemporal dynamics.

Kevin Zeng, Michael D. Graham

CoRR, 2021

2020

Low- and High-Drag Intermittencies in Turbulent Channel Flows.

Rishav Agrawal, Henry C.-H. Ng, Ethan A. Davis, Jae Sung Park, Michael D. Graham, David J. C. Dennis, Robert J. Poole

Entropy, 2020

Deep learning to discover and predict dynamics on an inertial manifold.

Alec J. Linot, Michael D. Graham

CoRR, 2020

2012

Accelerated boundary integral method for multiphase flow in non-periodic geometries.

Amit Kumar, Michael D. Graham

J. Comput. Phys., 2012