Physics-Informed Neural Networks for Shell Structures

TL;DR

This paper introduces a physics-informed neural network approach for modeling thin shell structures, demonstrating its accuracy and potential as a simplified alternative to traditional finite element methods, especially in locking-free scenarios.

Contribution

It presents a novel PINN framework for shell structures based on Naghdi's theory, capable of handling non-Euclidean domains and addressing locking issues.

Findings

PINN accurately predicts shell responses in benchmarks.

Weak form solutions outperform strong form in accuracy.

Training time increases in thin-thickness limit due to numerical stiffness.

Abstract

The numerical modeling of thin shell structures is a challenge, which has been met by a variety of finite element (FE) and other formulations -- many of which give rise to new challenges, from complex implementations to artificial locking. As a potential alternative, we use machine learning and present a Physics-Informed Neural Network (PINN) to predict the small-strain response of arbitrarily curved shells. To this end, the shell midsurface is described by a chart, from which the mechanical fields are derived in a curvilinear coordinate frame by adopting Naghdi's shell theory. Unlike in typical PINN applications, the corresponding strong or weak form must therefore be solved in a non-Euclidean domain. We investigate the performance of the proposed PINN in three distinct scenarios, including the well-known Scordelis-Lo roof setting widely used to test FE shell elements against locking. Results show that the PINN can accurately identify the solution field in all three benchmarks if the equations are presented in their weak form, while it may fail to do so when using the strong form. In the thin-thickness limit, where classical methods are susceptible to locking, training time notably increases as the differences in scaling of the membrane, shear, and bending energies lead to adverse numerical stiffness in the gradient flow dynamics. Nevertheless, the PINN can accurately match the ground truth and performs well in the Scordelis-Lo roof benchmark, highlighting its potential for a drastically simplified alternative to designing locking-free shell FE formulations.