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

We introduce and test a general machine-learning-based technique for theinference of short term causal dependence between state variables of an unknowndynamical system from time series measurements of its state variables. Ourtechnique leverages the results of a machine learning process for short timeprediction to achieve our goal. The basic idea is to use the machine learningto estimate the elements of the Jacobian matrix of the dynamical flow along anorbit. The type of machine learning that we employ is reservoir computing. Wepresent numerical tests on link inference of a network of interacting dynamicalnodes. It is seen that dynamical noise can greatly enhance the effectiveness ofour technique, while observational noise degrades the effectiveness. We believethat the competition between these two opposing types of noise will be the keyfactor determining the success of causal inference in many of the mostimportant application situations.

Using Machine Learning to Assess Short Term Causal Dependence and Infer Network Links