Abstract
Element stiffness matrices in the FEM are usually calculated using Gauss-Legendre quadrature. It is well known that the accuracy of the quadrature of an element depends on the shape of the element. Deep learning can be used to predict optimal quadrature parameters for each element to improve the accuracy of its numerical quadrature. To prepare training patterns for deep learning, we can use various metaheuristic algorithms, such as GA and PSO, for solving optimization problems in order to find optimal quadrature parameters for a lot of elements of various shape. Considering the use of the results of the search as training patterns for deep learning, however, additional constraints should be taken into account. In this paper, the feasibility of some metaheuristic algorithms for finding optimal quadrature parameters is investigated.
Published Version
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