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20152026
most citedMachine-learning inference of fluid variables from data using reservoir computing

83 citations · 260 across the 21 of their papers we have counts for

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Showing 2018Show all

5 papers · 1 filter

math.DS2018★ 1 cited

Quasiperiodic orbits in Siegel disks/balls and the Babylonian problem

Yoshitaka Saiki, James A. Yorke

We investigate numerically complex dynamical systems where a fixed point is surrounded by a disk or ball of quasiperiodic orbits, where there is a change of variables (or conjugacy…

math.DS2018★ 28 cited

Piecewise-linear maps with heterogeneous chaos

Yoshitaka Saiki, Hiroki Takahasi, James A. Yorke

Chaotic dynamics can be quite heterogeneous in the sense that in some regions the dynamics are unstable in more directions than in other regions. When trajectories wander between t…

nlin.CD2018★ 16 cited

Low-dimensional paradigms for high-dimensional hetero-chaos

Yoshitaka Saiki, Miguel A. F. Sanjuan, James A. Yorke

The dynamics on a chaotic attractor can be quite heterogeneous, being much more unstable in some regions than others. Some regions of a chaotic attractor can be expanding in more d…

physics.comp-ph2018★ 83 cited

Machine-learning inference of fluid variables from data using reservoir computing

Kengo Nakai, Yoshitaka Saiki

We infer both microscopic and macroscopic behaviors of a three-dimensional chaotic fluid flow using reservoir computing. In our procedure of the inference, we assume no prior knowl…

nlin.CD2018

The continuous route to multi-chaos

Yoshitaka Saiki, Miguel A. F. Sanjuan, James A. Yorke

For low-dimensional chaotic attractors there is usually a single number of unstable dimensions for all of its periodic orbits and we can say such attractors exhibit "mono-chaos". I…