Computational Methods for Stochastic Dynamics

Co-organised by: Ioannis P. Mitseas1,5, Ioannis A. Kougioumtzoglou2, Michael Beer3, Jianbing Chen4

                                                

1School of Civil Engineering, University of Leeds, UK
2Department of Civil Engineering and Engineering Mechanics, Columbia University, USA
3Institute for Risk and Reliability, Leibniz Universita╠łt Hannover, Germany
4School of Civil Engineering, Tongji University, China
5School of Civil Engineering, National Technical University of Athens, Greece

                                   
E-mail: I.Mitseas@leeds.ac.uk, ikougioum@columbia.edu, beer@irz.uni-hannover.de, chenjb@tongji.edu.cn                   


The proper quantitative treatment of uncertainties is a fundamental prerequisite for determining reliable estimates of the response and reliability statistics of diverse engineering systems. In particular, efficient analysis and design procedures dictate the utilization of potent mathematical tools to treat complex system and excitation modeling. In fact, ever-increasing computational capabilities, novel signal processing techniques, and advanced experimental setups have contributed to highly complex governing equations from a mathematics perspective. These include nonlinearities, hysteretic terms, joint time-frequency representations, as well as fractional derivatives. Note that the solution of such equations is an open issue and an active research topic. Clearly, considering also stochastic effects in the system governing dynamics renders the solution of the corresponding equations of motion a computationally challenging task.

                   

The objective of this MS is to present recent advances and emerging cross-disciplinary approaches in the broad field of computational methods for stochastic engineering dynamics problems with a focus on uncertainty modeling and propagation. 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, as well as data-driven and signal processing methodologies in the modeling process are welcome. A non-exhaustive list includes joint time-frequency analysis tools, sparse representations, compressive sampling and data driven approaches, stochastic/fractional calculus modeling and applications, nonlinear stochastic dynamics, stochastic model/dimension reduction techniques, Monte Carlo simulation methods, and risk/reliability assessment applications.

                                   

    

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