Dcm: Deep Complementary Energy Method Based on the Principle of Minimum Complementary Energy

Abstract

The principle of minimum potential and complementary energy are the most important variational principles in solid mechanics. The deep energy method (DEM), which has received much attention, is based on the principle of minimum potential energy, but it lacks the important form of minimum complementary energy. To fill the gap, we propose a deep complementary energy method (DCM) based on the principle of minimum complementary energy. The output function of DCM is the stress function that naturally satisfies the equilibrium equation. We extend the proposed DCM algorithm to DCM-Plus (DCM-P), adding the terms that naturally satisfy the biharmonic equation in the Airy stress function. We combine operator learning with physical equations and propose a deep complementary energy operator method (DCM-O),  including branch net, trunk net, basis net, and particular net. DCM-O first combines existing high-fidelity numerical results to train DCM-O through data. Then the complementary energy is used to train the branch net and trunk net in DCM-O. To analyze DCM performance, we present the numerical result of the most common stress functions, the Prandtl and Airy stress function. The proposed method DCM is used to model the representative mechanical problems with different types of boundary conditions. We compare DCM with the existing PINNs and DEM algorithms. The result shows the advantage of the proposed DCM is suitable for dealing with problems of dominated displacement boundary conditions, which is proved by mathematical derivations, as well as with numerical experiments. DCM-P and DCM-O can improve the accuracy and efficiency of DCM. DCM is an essential supplementary energy form to the deep energy method. Operator learning based on the energy method can balance data and physical equations well, giving computational mechanics broad research prospects.

Keywords: Physics-informed neural network, deep energy method, operator regression, complementary energy, DeepONet, Deep learning