MS Title:
Uncertainty Modelling and Computational Challenges in Stochastic Dynamics

Description:
In the ever-evolving field of engineering, ensuring the reliability of structural systems is of paramount importance. Addressing the complex buildings and structures subjected to stochastic excitations, this mini-symposium highlights the importance of accounting for uncertainties, the design and modelling of input loads, and the utilization of advanced computational techniques to enhance the ability to tackle challenges in stochastic dynamics. Engineering systems are often featuring complex nonlinearities and intricate time-frequency representations. State-of-the-art modelling approaches allow for the comprehensive understanding of these complexities, resulting in significantly more precise predictions of structural stochastic behavior. Further, uncertainties are inherent in engineering problems, and their accurate consideration is vital for the dependable assessment of structural reliability. The objective of this mini-symposium is to explore innovative approaches and methodologies for characterizing, quantifying, and incorporating uncertainties into stochastic dynamics. Furthermore, the emphasis will be on approaches for uncertain load modelling and advanced computational methods, including probabilistic dependency characterization and complex spatiotemporal variability representation for various stochastic processes (fields), highlighting their pivotal role in achieving reliable simulation results. This mini-symposium will feature novel research on how computational tools and techniques can be leveraged to enhance the capacity to solve complex structural reliability problems.

Session Chairs:
Marco Behrendt, Leibniz Universität Hannover, Hannover, Germany. E-mail: behrendt@irz.uni-hannover.de
Meng-Ze Lyu, Tongji University, Shanghai, China. E-mail: lyumz@tongji.edu.cn
Jian-Bing Chen, Tongji University, Shanghai, China. E-mail: chenjb@tongji.edu.cn
Michael Beer, Leibniz Universität Hannover, Hannover, Germany. E-mail: beer@irz.unihannover.de