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

Origami structures often serve as the building block of mechanical systemsdue to their rich static and dynamic behaviors. Experimental observation andtheoretical modeling of origami dynamics have been reported extensively,whereas the data-driven modeling of origami dynamics is still challenging dueto the intrinsic nonlinearity of the system. In this study, we show how thedynamic mode decomposition (DMD) method can be enhanced by integrating geometryinformation of the origami structure to model origami dynamics in an efficientand accurate manner. In particular, an improved version of DMD with control,that we term geometry-informed dynamic mode decomposition~(giDMD), is developedand evaluated on the origami chain and dual Kresling origami structure toreveal the efficacy and interpretability. We show that giDMD can accuratelypredict the dynamics of an origami chain across frequencies, where thetopological boundary state can be identified by the characteristics of giDMD.Moreover, the periodic intrawell motion can be accurately predicted in the dualorigami structure. The type of dynamics in the dual origami structure can alsobe identified. The model learned by the giDMD also reveals the influentialgeometrical parameters in the origami dynamics, indicating the interpretabilityof this method. The accurate prediction of chaotic dynamics remains a challengefor the method. Nevertheless, we expect that the proposed giDMD approach willbe helpful towards the prediction and identification of dynamics in complexorigami structures, while paving the way to the application to a wider varietyof lightweight and deployable structures.

Geometry-informed dynamic mode decomposition in origami dynamics