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£136 today · previous high £136 · all-time low £129

Springer Computational Mechanics with Neural Networks - Engineering Guide

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Description

Computational Mechanics with Neural Networks is a comprehensive lecture notes resource published by Springer that bridges classical numerical methods in engineering with modern neural network approaches. This volume, part of Springer's respected Lecture Notes on Numerical Methods in Engineering and Sciences series, provides engineers and researchers with essential theory and practical applications for solving complex mechanical problems using artificial intelligence techniques. The text combines rigorous mathematical foundations with neural network implementations, enabling professionals to develop efficient computational solutions for structural analysis, fluid dynamics, material modelling, and other engineering challenges. Ideal for graduate students, academic researchers, and industry practitioners seeking to integrate machine learning into traditional computational mechanics workflows.

Key Features

Springer publication combining classical numerical methods with contemporary neural network applications in engineering mechanics.

Part of the established Lecture Notes on Numerical Methods in Engineering and Sciences series, ensuring academic rigour and peer-reviewed content.

Suitable for graduate-level study and professional reference in computational engineering, finite element analysis, and machine learning applications.

Enables integration of neural networks into traditional engineering simulations and modelling workflows.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
27 February 2022
Listed Since
01 February 2022

Barcode

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