231 citations · 1.9k across the 42 of their papers we have counts for
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Deep learning delay coordinate dynamics for chaotic attractors from partial observable data
Charles D. Young, Michael D. Graham
A common problem in time series analysis is to predict dynamics with only scalar or partial observations of the underlying dynamical system. For data on a smooth compact manifold,…
Data-driven low-dimensional dynamic model of Kolmogorov flow
Carlos E. Pérez De Jesús, Michael D. Graham
Reduced order models (ROMs) that capture flow dynamics are of interest for decreasing computational costs for simulation as well as for model-based control approaches. This work pr…
Data-driven control of spatiotemporal chaos with reduced-order neural ODE-based models and reinforcement learning
Kevin Zeng, Alec J. Linot, Michael D. Graham
Deep reinforcement learning (RL) is a data-driven method capable of discovering complex control strategies for high-dimensional systems, making it promising for flow control applic…
Stabilized Neural Ordinary Differential Equations for Long-Time Forecasting of Dynamical Systems
Alec J. Linot, Joshua W. Burby, Qi Tang +3
In data-driven modeling of spatiotemporal phenomena careful consideration often needs to be made in capturing the dynamics of the high wavenumbers. This problem becomes especially…