Host: The Japan Society of Mechanical Engineers
Name : [in Japanese]
Date : October 25, 2023 - October 27, 2023
Physics-informed neural network (PINNs or PINN) is supervised learning for approximating the initial-boundary value problems of partial differential equations. This research focuses on the magnetostatic field problem derived from Maxwell's equations and evaluate the performance of large-scale computation and parallel computation of PINN. In finite element analysis, which is a conventional numerical analysis method, the magnetostatic field is formulated with the magnetic vector potential ‘A’ as an unknown. However, since the three-dimensional magnetostatic field problem has indefiniteness, it is necessary to solve a singular problem or construct an equation with the indefiniteness removed. This study also applies PINN to partial differential equations based on the A-formulation.
