MS 01

Computational Methods and Applications for Stochastic Engineering Dynamics


V. C. Fragkoulis,

Institut für Risiko und Zuverlässigkeit, Leibniz Universität Hannover, Germany

J. Xu,

College of Civil Engineering, Hunan University, China

F. Kong,

School of Civil Engineering and Architecture, Wuhan University of Technology, China

M. Beer,

Institut für Risiko und Zuverlässigkeit, Leibniz Universität Hannover, Germany

Institute for Risk and Uncertainty, University of Liverpool, UK

International Joint Research Center for Engineering Reliability and Stochastic Mechanics, Tongji University, China

I. A. Kougioumtzoglou,

Department of Civil Engineering and Engineering Mechanics, Columbia University, USA


The efficient propagation of the uncertainties of complex engineering systems constitutes one of the major challenges associated with uncertainty quantification in the field of stochastic dynamics of structural and mechanical systems. In addition, recent advances in stochastic dynamics and emerging technologies, such as in nano-mechanics and energy harvesting, dictate a highly sophisticated modeling of the related systems and corresponding excitations. In this regard, the necessity of employing novel mathematical tools and potent signal processing techniques, which lead to efficient solution frameworks for studying the behavior of engineering systems and assessing their reliability, is evident. The objective of this MS is to present recent advances and emerging cross-disciplinary approaches in the broad field of computational methods of stochastic engineering dynamics. Further, this MS intends to provide a forum for a fruitful exchange of ideas and interaction among diverse technical and scientific disciplines. Specific contributions related both to fundamental research and to engineering applications of stochastic dynamics and signal processing methodologies are welcome. A non-exhaustive list includes joint time/frequency analysis tools, sparse representation-based methodologies, stochastic/fractional calculus modeling and applications, nonlinear stochastic dynamics, stochastic model/dimension reduction techniques, Monte Carlo simulation methods, and risk/reliability assessment applications.