{"id":15812,"date":"2026-08-10T11:58:51","date_gmt":"2026-08-10T11:58:51","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/springer-deep-learning-in-computational-mechanics-second-edition-2025\/"},"modified":"2026-08-10T11:58:51","modified_gmt":"2026-08-10T11:58:51","slug":"springer-deep-learning-in-computational-mechanics-second-edition-2025","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/springer-deep-learning-in-computational-mechanics-second-edition-2025\/","title":{"rendered":"Springer \u2013 Deep Learning in Computational Mechanics, Second Edition 2025"},"content":{"rendered":"
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Description<\/span><\/h2>\n

Deep Learning in Computational Mechanics is intended for students, engineers, and researchers interested in the convergence of computational mechanics and deep learning. The main goal of this book is to introduce the mathematical and computational foundations of deep learning in an understandable way, accompanied by precise mathematical formulas, so that mechanics professionals can harness the power of neural networks in their work. This book attempts to fill an important scientific gap and integrate traditional models of classical mechanics with data-driven approaches.<\/span><\/p>\n

In this book, the authors describe the diverse applications of deep learning in computational mechanics, while also providing detailed explanations of the fundamental principles of related computational mechanics. This dual approach allows engineers with less experience in the field of deep learning to understand its core concepts, such as neural networks and various architectures, in the context of mechanical problems. The book also discusses concrete applications, such as approximate modeling of stress-strain relationships and prediction of deformation under loading.<\/span><\/p>\n

Book Features<\/span><\/h3>\n