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05/01/2023
Autoencoders for discovering manifold dimension and coordinates in data from complex dynamical systems
While many phenomena in physics and engineering are formally high-dimens...
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01/28/2023
Turbulence control in plane Couette flow using low-dimensional neural ODE-based models and deep reinforcement learning
The high dimensionality and complex dynamics of turbulent flows remain a...
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01/11/2023
Dynamics of a data-driven low-dimensional model of turbulent minimal Couette flow
Because the Navier-Stokes equations are dissipative, the long-time dynam...
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11/20/2022
Deep learning delay coordinate dynamics for chaotic attractors from partial observable data
A common problem in time series analysis is to predict dynamics with onl...
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10/29/2022
Data-driven low-dimensional dynamic model of Kolmogorov flow
Reduced order models (ROMs) that capture flow dynamics are of interest f...
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05/01/2022
Data-driven control of spatiotemporal chaos with reduced-order neural ODE-based models and reinforcement learning
Deep reinforcement learning (RL) is a data-driven method capable of disc...
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08/31/2021
Data-Driven Reduced-Order Modeling of Spatiotemporal Chaos with Neural Ordinary Differential Equations
Dissipative partial differential equations that exhibit chaotic dynamics...
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08/12/2021
Charts and atlases for nonlinear data-driven models of dynamics on manifolds
We introduce a method for learning minimal-dimensional dynamical models ...
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04/09/2021
