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Found 7 papers, 5 papers with code
Phase autoencoder for limit-cycle oscillators
We present a phase autoencoder that encodes the asymptotic phase of a limit-cycle oscillator, a fundamental quantity characterizing its synchronization dynamics.
Grasping Extreme Aerodynamics on a Low-Dimensional Manifold
We reveal that the nonlinear vortical flow field over time and parameter space can be compressed to only three variables with a lift-augmented autoencoder while holding the essence of the original high-dimensional physics.
Super-Resolution Analysis via Machine Learning: A Survey for Fluid Flows
This paper surveys machine-learning-based super-resolution reconstruction for vortical flows.
Distributed Control of Partial Differential Equations Using Convolutional Reinforcement Learning
We present a convolutional framework which significantly reduces the complexity and thus, the computational effort for distributed reinforcement learning control of dynamical systems governed by partial differential equations (PDEs).
Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learning
This reconstruction problem is especially difficult when sensors are sparsely positioned in a seemingly random or unorganized manner, which is often encountered in a range of scientific and engineering problems.
Probabilistic neural networks for fluid flow surrogate modeling and data recovery
We consider the use of probabilistic neural networks for fluid flow surrogate modeling and data recovery.
Fluid Dynamics
Modal Analysis of Fluid Flows: An Overview
Simple aerodynamic configurations under even modest conditions can exhibit complex flows with a wide range of temporal and spatial features.
Fluid Dynamics
