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Computational mechanics enhanced by deep learning
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DOI：10.1016/j.cma.2017.08.040.png)
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Abstract
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The present paper describes a method to enhance the capability of, or to broaden the scope of computational mechanics by using deep learning, which is one of the machine learning methods and is based on the artificial neural network. The method utilizes deep learning to extract rules inherent in a computational mechanics application, which usually are implicit and sometimes too complicated to grasp from the large amount of available data A new method of numerical quadrature for the FEM stiffness matrices is developed by using the proposed method, where a kind of optimized quadrature rule superior in accuracy to the standard Gauss-Legendre quadrature is obtained on the element-by-element basis. The detailed formulation of the proposed method is given with the sample application above, and an acceleration technique for the proposed method is discussed. (C) 2017 Elsevier B.V. All rights reserved.
Keywords:
Deep learning
Artificial neural network
Numerical quadrature
Element stiffness matrix
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