231 citations · 1.9k across the 42 of their papers we have counts for
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cs.LG2021★ 61 cited
Data-Driven Reduced-Order Modeling of Spatiotemporal Chaos with Neural Ordinary Differential Equations
Alec J. Linot, Michael D. Graham
Dissipative partial differential equations that exhibit chaotic dynamics tend to evolve to attractors that exist on finite-dimensional manifolds. We present a data-driven reduced o…
cs.LG2021★ 105 cited
Data-driven discovery of intrinsic dynamics
Daniel Floryan, Michael D. Graham
Dynamical models underpin our ability to understand and predict the behavior of natural systems. Whether dynamical models are developed from first-principles derivations or from ob…
cs.LG2021
Symmetry reduction for deep reinforcement learning active control of chaotic spatiotemporal dynamics
Kevin Zeng, Michael D. Graham
Deep reinforcement learning (RL) is a data-driven, model-free method capable of discovering complex control strategies for macroscopic objectives in high-dimensional systems, makin…