Multi-level physics informed deep learning for solving partial differential equations in computational structural mechanics.

ABSTRACT: Physics-informed neural network has emerged as a promising approach for solving partial differential equations. However, it is still a challenge for the computation of structural mechanics problems since it involves solving higher-order partial differential equations as the governing equations are fourth-order nonlinear equations. Here we develop a multi-level physics-informed neural network framework where an aggregation model is developed by combining multiple neural networks, with each one involving only first-order or second-order partial differential equations representing different physics information such as geometrical, constitutive, and equilibrium relations of the structure. The proposed framework demonstrates a remarkable advancement over the classical neural networks in terms o

SUBMITTER: He W 

PROVIDER: S-EPMC11530524 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature