arXiv subjects

James A Yorke

Publications and source records attributed to James A Yorke.

5 recordsLinked to original sources

arXiv2024

Ambiguity in the use of SIR models to fit epidemic incidence data

When fitting a multi-parameter model to a data set, computer algorithms may suggest that a range of parameters provide equally reasonable fits, making the parameter estimation difficult. Here, we prove this fact for an SIR model. We say a set of parameter values is a good fit to outbreak data if the solution has the data's three most significant characteristics: the standard deviation, the mean time, and the total number of cases. In our model, in addition to the "basic reproduction number" $R_0$, three other parameters need to be estimated to fit a solution to outbreak data. We will show that those parameters can be chosen so that each gives a linear transformation of a solution's incidence data. As a result, we show that for every choice of $R_0>1$, there is a good fit for each outbreak. We also illustrate our results by providing the least square best fits of the New York City and London data sets of the Omicron variant of COVID-19. Furthermore, we show how versions of the SIR model with $N$ compartments have far more good fits- - indeed a high dimensional set of good fits -- for each target -- showing that more complicated models may have an even greater problem in overparametrizing outbreak characteristics.

q-bio.PE↗

arXiv2017

Multi-chaos from Quasiperiodicity

One of the common characteristics of chaotic maps or flows in high dimensions is "unstable dimensional variability", in which there are periodic points whose unstable manifolds have different dimensions. In this paper, in trying to characterize such systems we define a property called "multi-chaos". A set $X$ is multi-chaotic if $X$ has a dense trajectory and for at least 2 values of $k$, the $k$-dimensionally unstable periodic points are dense in $X$. All proofs that such a behavior holds have been based on hyperbolicity in the sense that (i) there is a chaotic set $X$ with a dense trajectory and (ii) in X there are two or more hyperbolic sets with different unstable dimensions. We present a simple 2-dimensional paradigm for multi-chaos in which a quasiperiodic orbit plays the key role, replacing the large hyperbolic set.

math.DS↗

arXiv2017

Quantitative Quasiperiodicity

The Birkhoff Ergodic Theorem concludes that time averages, i.e., Birkhoff averages, $Σ_{n=0}^{N-1} f(x_n)/N$ of a function $f$ along a length $N$ ergodic trajectory $(x_n)$ of a function $T$ converge to the space average $\int f dμ$, where $μ$ is the unique invariant probability measure. Convergence of the time average to the space average is slow. We introduce a modified average of $f(x_n)$ by giving very small weights to the "end" terms when $n$ is near $0$ or $N-1$. When $(x_n)$ is a trajectory on a quasiperiodic torus and $f$ and $T$ are $C^\infty$, we show that our weighted Birkhoff averages converge 'super" fast to $\int f dμ$ with respect to the number of iterates $N$, i.e. with error decaying faster than $N^{-m}$ for every integer $m$. Our goal is to show that our weighted Birkhoff average is a powerful computational tool, and this paper illustrates its use for several examples where the quasiperiodic set is one or two dimensional. In particular, we compute rotation numbers and conjugacies (i.e. changes of variables) and their Fourier series, often with 30-digit accuracy.

math.DS↗

arXiv2017

Solving the Babylonian Problem of quasiperiodic rotation rates

A trajectory $u_n := F^n(u_0), n = 0,1,2, \dots $ is quasiperiodic if the trajectory lies on and is dense in some $d$-dimensional torus, and there is a choice of coordinates on the torus $\mathbb{T}$ for which $F$ has the form $F(θ) = θ+ ρ\bmod1$ for all $θ\in\mathbb{T}$ and for some $ρ\in\mathbb{T}$. There is an ancient literature on computing three rotation rates $ρ$ for the Moon. %There is a literature on determining the coordinates of the vector $ρ$, called the rotation rates of $F$. (For $d>1$ we always interpret $\bmod1$ as being applied to each coordinate.) However, even in the case $d=1$ there has been no general method for computing $ρ$ given only the trajectory $u_n$, though there is a literature dealing with special cases. Here we present our Embedding Continuation Method for computing some components of $ρ$ from a trajectory. It is based on the Takens Embedding Theorem and the Birkhoff Ergodic Theorem. Rotation rates are often called "rotation numbers" and both refer to a rate of rotation of a circle. However, the coordinates of $ρ$ depend on the choice of coordinates of $\mathbb{T}$. We explore the various sets of possible rotation rates that $ρ$ can yield. We illustrate our ideas with examples in dimensions $d=1$ and $2$.

math.DS↗

arXiv2007

Turbulence transition and the edge of chaos in pipe flow

The linear stability of pipe flow implies that only perturbations of sufficient strength will trigger the transition to turbulence. In order to determine this threshold in perturbation amplitude we study the \emph{edge of chaos} which separates perturbations that decay towards the laminar profile and perturbations that trigger turbulence. Using the lifetime as an indicator and methods developed in (Skufca et al, Phys. Rev. Lett. {\bf 96}, 174101 (2006)) we show that superimposed on an overall $1/\Re$-scaling predicted and studied previously there are small, non-monotonic variations reflecting folds in the edge of chaos. By tracing the motion in the edge we find that it is formed by the stable manifold of a unique flow field that is dominated by a pair of downstream vortices, asymmetrically placed towards the wall. The flow field that generates the edge of chaos shows intrinsic chaotic dynamics.

nlin.CD↗