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Found 10 papers, 0 papers with code
Breaking Symmetries of the Reservoir Equations in Echo State Networks
Further, four ways to break the symmetry are compared numerically: A bias in the output, a shift in the input, a quadratic term in the readout, and a mixture of even and odd activation functions.
Controlling nonlinear dynamical systems into arbitrary states using machine learning
We propose a novel and fully data driven control scheme which relies on machine learning (ML).
Predicting high-dimensional heterogeneous time series employing generalized local states
We generalize the concept of local states (LS) for the prediction of high-dimensional, potentially mixed chaotic systems.
Time Series Analysis
Data Analysis, Statistics and Probability
Chaotic Dynamics
Computational Physics
Heartbeat instability as auto-oscillation between dim and bright void regimes
In the bright regime, a time-averaged electric field at the void boundary heats the electrons causing bright plasma emission inside the void.
Plasma Physics
Exploring the limits of multifunctionality across different reservoir computers
Multifunctional neural networks are capable of performing more than one task without changing any network connections.
Controlling dynamical systems to complex target states using machine learning: next-generation vs. classical reservoir computing
On the example of forcing a chaotic parametrization of the Lorenz system into intermittent dynamics, we show first that classical reservoir computing excels at this task.
Weight fluctuations in (deep) linear neural networks and a derivation of the inverse-variance flatness relation
We investigate the stationary (late-time) training regime of single- and two-layer linear underparameterized neural networks within the continuum limit of stochastic gradient descent (SGD) for synthetic Gaussian data.
Forecasting Trends in Food Security: a Reservoir Computing Approach
The methodology we introduce establishes the groundwork for a global, data-driven early warning system designed to anticipate and detect food insecurity.
Extrapolating tipping points and simulating non-stationary dynamics of complex systems using efficient machine learning
We propose a novel, fully data-driven machine learning algorithm based on next-generation reservoir computing to extrapolate the bifurcation behavior of nonlinear dynamical systems using stationary training data samples.
Linear and nonlinear causality in financial markets
Identifying and quantifying co-dependence between financial instruments is a key challenge for researchers and practitioners in the financial industry.
