English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In this paper, we consider the problem of parameter sensitivity in models ofcomplex dynamical systems through the lens of information geometry. Wecalculate the sensitivity of model behavior to variations in parameters. Inmost cases, models are sloppy, that is, exhibit an exponential hierarchy ofparameter sensitivities. We propose a parameter classification scheme based onhow the sensitivities scale at long observation times. We show that foroscillatory models, either with a limit cycle or a strange attractor,sensitivities can become arbitrarily large, which implies a higheffective-dimensionality on the model manifold. Sloppy models with a singlefixed point have model manifolds with low effective-dimensionality, previouslydescribed as a "hyper-ribbon". In contrast, models with high effectivedimensionality translate into multimodal fitting problems. We define a measureof curvature on the model manifold which we call the \emph{winding frequency}that estimates the linear density of local minima in the model's parameterspace. We then show how alternative choices of fitting metrics can "unwind" themodel manifold and give low winding frequencies. This prescription translatesthe model manifold from one of high effective-dimensionality into the"hyper-ribbon" structures observed elsewhere. This translation opens the doorfor applications of sloppy model analysis and model reduction methods developedfor models with low effective-dimensionality.

Unwinding the model manifold: choosing similarity measures to remove local minima in sloppy dynamical systems