[PAST EVENT] Honors Thesis Defense - Alexander Berliner
Title: Period Doubling Cascades from Data
Orbit diagrams of period doubling cascades represent systems going from periodicity to chaos. Here, we investigate whether a Gaussian process regression can be used to approximate a system from data and recover asymptotic dynamics in the orbit diagrams for period doubling cascades. To compare the orbits of a system to the approximation, we compute the Wasserstein metric between the point clouds of their obits for varying bifurcation parameter values. Visually comparing the period doubling cascades, we note that the exact bifurcation values may shift, which is confirmed in the plots of the Wasserstein distance. This has implications for studying dynamics from time series data since an approximation of a system’s period doubling cascade may lead to unpredictable model behavior in a neighborhood of the true bifurcation parameter value.
Also presented via Zoom (open to the public): https://cwm.zoom.us/j/94641648530
Meeting ID: 946 4164 8530