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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Sparse model identification enables the discovery of nonlinear dynamicalsystems purely from data; however, this approach is sensitive to noise,especially in the low-data limit. In this work, we leverage the statisticalapproach of bootstrap aggregating (bagging) to robustify the sparseidentification of nonlinear dynamics (SINDy) algorithm. First, an ensemble ofSINDy models is identified from subsets of limited and noisy data. Theaggregate model statistics are then used to produce inclusion probabilities ofthe candidate functions, which enables uncertainty quantification andprobabilistic forecasts. We apply this ensemble-SINDy (E-SINDy) algorithm toseveral synthetic and real-world data sets and demonstrate substantialimprovements to the accuracy and robustness of model discovery from extremelynoisy and limited data. For example, E-SINDy uncovers partial differentialequations models from data with more than twice as much measurement noise ashas been previously reported. Similarly, E-SINDy learns the Lotka Volterradynamics from remarkably limited data of yearly lynx and hare pelts collectedfrom 1900-1920. E-SINDy is computationally efficient, with similar scaling asstandard SINDy. Finally, we show that ensemble statistics from E-SINDy can beexploited for active learning and improved model predictive control.

Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control