Computational Mechanics with Deep Learning: An Introduction (Lecture Notes on Numerical Methods in Engineering and Sciences) - Softcover

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Genki Yagawa received his Ph.D. from University of Tokyo in 1970. He became Professor at University of Tokyo in 1984 and Director and Professor at Center for Computational Mechanics Research of Toyo University in 2004. Currently, he is an Emeritus Professor at University of Tokyo and Toyo University, Chairman of Nuclear Safety Research Association, and Member of Science Council of Japan. His awards and honors include the Order of the Sacred Treasure, Gold Rays with Neck Ribbon endowed from His Majesty the Japanese Emperor, Japan Academy Prize, International Association for Computational Mechanics Award, Asia Pacific Association Computational Mechanics Zienkiewicz Medal, Prime Minister Award, Minister of Science and Technology Award, Toray Science and Technology Medal, Honorary Doctor Endowed from Iasi Technical University, and Fellow of International Association for Computational Mechanics, Japan Society for Industrial and Applied Mathematics, Japan Society for Simulation Technology and Atomic Energy Society of Japan.

Atsuya Oishi received his Ph.D. from University of Tokyo in 1996. He became Lecturer at University of Tokushima in 1997 and has been an Associate Professor at University of Tokushima since 2006. His awards include the outstanding paper award from Japan Society for Computational Engineering and Science and JACM fellow award from Japan Association for Computational Mechanics.

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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.