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cs.LG2023
Extrapolating tipping points and simulating non-stationary dynamics of complex systems using efficient machine learning
Daniel Köglmayr, Christoph Räth
Model-free and data-driven prediction of tipping point transitions in nonlinear dynamical systems is a challenging and outstanding task in complex systems science. We propose a nov…
cs.LG2023
Weight fluctuations in (deep) linear neural networks and a derivation of the inverse-variance flatness relation
Markus Gross, Arne P. Raulf, Christoph Räth
We investigate the stationary (late-time) training regime of single- and two-layer underparameterized linear neural networks within the continuum limit of stochastic gradient desce…
cs.LG2023
Controlling dynamical systems to complex target states using machine learning: next-generation vs. classical reservoir computing
Alexander Haluszczynski, Daniel Köglmayr, Christoph Räth
Controlling nonlinear dynamical systems using machine learning allows to not only drive systems into simple behavior like periodicity but also to more complex arbitrary dynamics. F…