Soft computing methods such as neural networks and genetic algorithms draw on the problem solving strategies of the natural world which differ fundamentally from the mathematically-based computing methods normally used in engineering. Human brains are highly effective computers with capabilities far beyond those of the most sophisticated electronic computers. The 'soft computing‘ methods they use can solve very difficult inverse problems based on reduction in disorder.
This book outlines these methods and applies them to a range of difficult engineering problems, including applications in computational mechanics, earthquake engineering, and engineering design. Most of these are difficult inverse problems – especially in engineering design – and are treated in depth.
Table of Contents
1 Soft computing 2 Neural networks 3 Neural networks in computational mechanics 4 Inverse problems in engineering 5 Autoprogressive algorithm and self-learning simulation 6 Evolutionary models 7 Implicit redundant representation in genetic algorithm 8 Inverse problem of engineering design
Jamshid Ghaboussi is Emeritus Professor in Civil and Environmental Engineering at University of Illinois at Urbana-Champaign. He received his doctoral degree from University of California at Berkeley. He has over 40 years of teaching and research experience in computational mechanics and soft computing with applications in structural engineering, geo-mechanics and bio-medical engineering. He has published extensively in these areas and is the inventor in five patents, mainly in the application of soft computing and computational mechanics. He is the co-author of books Numerical Methods in Computational Mechanics (CRC Press) and Nonlinear Computational Solid Mechanics (CRC Press). In recent years he has been conducting research on complex systems and has co-authored a book on Understanding Systems: A Grand Challenge for 21st Century Engineering (World Scientific Publishing).