Deep learning to discover and predict dynamics on an inertial manifold. - Alec J. Linot; Michael D. Graham (CoRR, 2020)

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)

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)

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)