Application of physics-informed machine learning methods in buckling design of axially compressed cylindrical shells

Abstract

A physics-informed artificial neural network (ANN) is developed for the buckling design of cylindrical shells under axial compression, and two strategies are applied to incorporate physical knowledge into the ANN model. One strategy is to introduce physics-informed features derived from the local reduced stiffness method (LRSM) as additional input features. The other strategy is to incorporate physical constraints based on elastic buckling theory into the loss function of ANN. The accuracy of the proposed model is verified using a buckling dataset of metal and non-metal cylindrical shells. Results demonstrate that the proposed physics-informed ANN model with four hidden layers achieves better predictive performance than pure data-driven random forest (RF), support vector machine (SVM) and ANN. Furthermore, the physics-informed model could predict moderately conservative buckling loads with a design factor of 1.25.