Titlebook: Computational Mechanics with Neural Networks; Genki Yagawa,Atsuya Oishi Book 2021 The Editor(s) (if applicable) and The Author(s), under e

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Deep Learning for Computational Mechanics neural networks are reviewed (Sect. .), then the applications of deep learning to the computational mechanics are shown (Sect. . for the applications of deep convolutional networks, and Sect. . for those of deep feedforward networks). Finally, the applications of miscellaneous deep networks to computational mechanics are discussed in Sect. ..

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Structural Optimization optimal design of materials (Sect. .), the optimization of production process (Sect. .), the control of dynamic behavior of structures (Sect. .), the subjective evaluation for handling and stability of vehicle (Sect. .), and many others related to this category (Sect. .).

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Other AI Technologies for Computational Mechanics manipulation (Sect. .), the contact search using genetic algorithm (Sect. .), the contact search using genetic programming (Sect. .), the non-linear equation systems solved with genetic algorithm (Sect. .), and other applications using various machine learning methods (Sects. .–.).

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Book 2021rning method in computational mechanics. Part II highlights the applications of neural networks to a variety of problems of computational mechanics. The final chapter gives perspectives to the applications of the deep learning to computational mechanics..

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Computational Mechanics with Neural Networks978-3-030-66111-3Series ISSN 1877-7341 Series E-ISSN 1877-735X

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https://doi.org/10.1007/978-981-10-4475-5rical quadrature of the elemental integration. Chapter 9 deals with this issue: Section . describes the classification of the finite elements based on the convergence speed in numerical quadrature, and Sect. . the optimization of quadrature parameters.

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