A physics-informed neural network for Kresling origami structures

Abstract

Origami structures have the advantages of foldability and adjustability, with applications spanning numerous engineering fields. However, there remains a dearth of intelligent and convenient methods that can effectively tackle both potential energy prediction and design problems on origami structures. This study proposes a novel physics-informed neural network (PINN) to predict and design potential energy curves of Kresling origami structures without labelled data. A sorting operation is coupled into the PINN, ensuring the prediction correctness. The accuracy of the potential energy curves predicted by the PINN is demonstrated through comparison with a reference and the exhaustive method. A prediction only takes less than one second and the precision of the PINN significantly surpasses that of the exhaustive method, proving the extremely high efficiency and credibility of the PINN. Furthermore, two design cases for Kresling origami structures, matching a target potential energy curve and a set of target potential energy points, are performed. The designed structures meet the expectations and each design takes a few seconds, showing the efficiency and applicability of the PINN in inverse design. The presented physics-driven approach without labelled data offers an innovative tool with learning ability to predict and design. It also provides a valuable reference for the force and stiffness design of Kresling origami structures. In addition, the code of the PINN is shared online.