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Learning Continuous Chaotic Attractors with a Reservoir Computer
Neural systems are well known for their ability to learn and store information as memories.
Teaching Recurrent Neural Networks to Modify Chaotic Memories by Example
Here we demonstrate that a recurrent neural network (RNN) can learn to modify its representation of complex information using only examples, and we explain the associated learning mechanism with new theory.
Using Machine Learning to Replicate Chaotic Attractors and Calculate Lyapunov Exponents from Data
For the case of the KS equation, we note that as the system's spatial size is increased, the number of Lyapunov exponents increases, thus yielding a challenging test of our method, which we find the method successfully passes.
Chaotic Dynamics
Reservoir observers: Model-free inference of unmeasured variables in chaotic systems
A scheme that accomplishes this is called an “observer.” We consider the case in which a model of the system is unavailable or insufficiently accurate, but “training” time series data of the desired state variables are available for a short period of time, and a limited number of other system variables are continually measured.
