Computational Mechanics with Deep Learning [electronic resource] : An Introduction / by Genki Yagawa, Atsuya Oishi.
Tipo de material: TextoSeries Lecture Notes on Numerical Methods in Engineering and SciencesEditor: Cham : Springer International Publishing : Imprint: Springer, 2023Edición: 1st ed. 2023Descripción: XIV, 402 p. 181 illus., 141 illus. in color. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9783031118470Tema(s): Mechanics, Applied | Computational intelligence | Artificial intelligence | Engineering Mechanics | Computational Intelligence | Artificial IntelligenceFormatos físicos adicionales: Printed edition:: Sin título; Printed edition:: Sin título; Printed edition:: Sin títuloClasificación CDD: 620.1 Clasificación LoC:TA349-359Recursos en línea: Libro electrónicoTipo de ítem | Biblioteca actual | Colección | Signatura | Copia número | Estado | Fecha de vencimiento | Código de barras |
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Libro Electrónico | Biblioteca Electrónica | Colección de Libros Electrónicos | 1 | No para préstamo |
Acceso multiusuario
1. Overview -- 2. Mathematical Background for Deep Learning -- 3. Computational Mechanics with Deep Learning -- 4. Numerical Quadrature with Deep Learning -- 5. Improvement of Finite Element Solutions with Deep Learning -- 6. Contact Mechanics with Deep Learning -- 7. Flow Simulation with Deep Learning -- 8. Further Applications with Deep Learning -- 9. Bases for Computer Programming -- 10. Computer Programming for a Representative Problem.
This book is intended for students, engineers, and researchers interested in both computational mechanics and deep learning. It presents the mathematical and computational foundations of Deep Learning with detailed mathematical formulas in an easy-to-understand manner. It also discusses various applications of Deep Learning in Computational Mechanics, with detailed explanations of the Computational Mechanics fundamentals selected there. Sample programs are included for the reader to try out in practice. This book is therefore useful for a wide range of readers interested in computational mechanics and deep learning.
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