Accelerated boundary integral method for multiphase flow in non-periodic geometries. - Amit Kumar; Michael D. Graham (J. Comput. Phys., 2012)

Deep learning to discover and predict dynamics on an inertial manifold. - Alec J. Linot; Michael D. Graham (CoRR, 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)

Symmetry reduction for deep reinforcement learning active control of chaotic spatiotemporal dynamics. - Kevin Zeng; Michael D. Graham (CoRR, 2021)

Charts and atlases for nonlinear data-driven models of dynamics on manifolds. - Daniel Floryan; Michael D. Graham (CoRR, 2021)

Data-Driven Reduced-Order Modeling of Spatiotemporal Chaos with Neural Ordinary Differential Equations. - Alec J. Linot; Michael D. Graham (CoRR, 2021)

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)

Data-driven low-dimensional dynamic model of Kolmogorov flow. - Carlos E. Pérez De Jesús; Michael D. Graham (CoRR, 2022)

Deep learning delay coordinate dynamics for chaotic attractors from partial observable data. - Charles D. Young; Michael D. Graham (CoRR, 2022)

Data-driven discovery of intrinsic dynamics. - Daniel Floryan; Michael D. Graham (Nat. Mac. Intell., 2022)

Dynamics of a data-driven low-dimensional model of turbulent minimal Couette flow. - Alec J. Linot; 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)

Autoencoders for discovering manifold dimension and coordinates in data from complex dynamical systems. - Kevin Zeng; 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)

Building symmetries into data-driven manifold dynamics models for complex flows. - Carlos E. Pérez De Jesús; Alec J. Linot; Michael D. Graham (CoRR, 2023)

Stabilized neural ordinary differential equations for long-time forecasting of dynamical systems. - Alec J. Linot; Joshua W. Burby; Qi Tang 001; Prasanna Balaprakash; Michael D. Graham; Romit Maulik (J. Comput. Phys., 2023)

Data-driven low-dimensional model of a sedimenting flexible fiber. - Andrew J. Fox; Michael D. Graham (CoRR, 2024)

On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions. - Jake Buzhardt; C. Ricardo Constante-Amores; Michael D. Graham (CoRR, 2024)

Blending data and physics for reduced-order modeling of systems with spatiotemporal chaotic dynamics. - Alex Guo; Michael D. Graham (CoRR, 2025)