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Found 4 papers, 1 papers with code
Autoencoders for discovering manifold dimension and coordinates in data from complex dynamical systems
Analysis of gradient descent dynamics for this architecture in the linear case reveals the role of the internal linear layers in leading to faster decay of a "collective weight variable" incorporating all layers, and the role of weight decay in breaking degeneracies and thus driving convergence along directions in which no decay would occur in its absence.
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 an obstacle to the discovery and implementation of control strategies.
Data-driven control of spatiotemporal chaos with reduced-order neural ODE-based models and reinforcement learning
We show that the ROM-based control strategy translates well to the true KSE and highlight that the RL agent discovers and stabilizes an underlying forced equilibrium solution of the KSE system.
Symmetry reduction for deep reinforcement learning active control of chaotic spatiotemporal dynamics
Many systems of flow control interest possess symmetries that, when neglected, can significantly inhibit the learning and performance of a naive deep RL approach.
