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We propose a deep learning based method for simulating the large bendingdeformation of bilayer plates. Inspired by the greedy algorithm, we propose apre-training method on a series of nested domains, which accelerate theconvergence of training and find the absolute minimizer more effectively. Theproposed method exhibits the capability to converge to an absolute minimizer,overcoming the limitation of gradient flow methods getting trapped in the localminimizer basins. We showcase better performance with fewer numbers of degreesof freedom for the relative energy errors and relative $L^2$-errors of theminimizer through numerical experiments. Furthermore, our method successfullymaintains the $L^2$-norm of the isometric constraint, leading to an improvementof accuracy.

A pre-training deep learning method for simulating the large bending deformation of bilayer plates