After successful completion of the course, students are able to...

• describe the mathematical foundations of soft computing
• analyze applications of soft computing in hydraulic engineering and structural engineering
• understand "artificial neural networks" and to know their use

Theory and practice - 30 hrs 3 ECTS credits Soft computing (SC)-artificial neural networks (ANNs), evolutionary algorithms and fuzzy inference systems is a counterpart of conventional (hard) methods of computing. The aim of the course is to present mathematical foundations of SC, to analyze various applications in hydro and structural engineering. This course describes from an engineering point of view a detailed treatment of ANNs. The most popular neural network architectures and learning algorithms for supervised and unsupervised learning are covered.

The course is supported with examples, numerical experiments and applications. The participants learn to implement and apply algorithms using Matlab. The course contents theory (50%) and practice (50%) equally to 30 hrs lectures and exercises.The subject matter is prepared at a level suitable for use for graduate, master and PhD students, post docs and researchers.

Learning the basics through lecture and self-study, supported with examples, numerical experiments and applications.

Written exam with exercises