MS06 Stochastic Engineering Dynamics: Recent Advances and Future Challenges
Prof.Ioannis A. Kougioumtzoglou: email@example.com
Ioannis A. Kougioumtzoglou, Department of Civil Engineering and Engineering Mechanics, Columbia University, USA, firstname.lastname@example.org
Antonina Pirrotta, Dipartimento di Ingegneria, University of Palermo, Italy, email@example.com
Radoslaw Iwankiewicz , West Pomeranian University of Technology in Szczecin, Poland, firstname.lastname@example.org
Arvid Naess, Department of Mathematical Sciences, NTNU, Trondheim, NO 7491, Norway, email@example.com
Abstract of the special session：
A major portion of the engineering dynamics/mechanics community has focused, with considerable success, on problems with stochastic media properties, random excitations and uncertain initial/boundary conditions. Nevertheless, the development of novel mathematical tools and of potent signal processing techniques, the ever-increasing available computational capabilities, and advanced experimental setups offer a unique novel tool for addressing complex problems for the first time and even posing new questions. Specifically, researchers and engineers are faced with the challenge of interpreting and translating measured data at multiple scales into pertinent stochastic models. In this regard, there is a need for developing robust multi-scale statistical descriptors and stochastic models capable of capturing complex uncertainty relationships. Further, there is a need for developing analytical/numerical methodologies for solving nonlinear high-dimensional stochastic (partial) differential equations efficiently, and for propagating uncertainty across various scales in the time and space domains.
The objective of this MS is to present recent advances and emerging cross-disciplinary approaches in the broad field of stochastic engineering dynamics/mechanics. 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 computational stochastic dynamics/mechanics and signal processing methodologies are welcome. A non-exhaustive list includes joint time/space-frequency analysis tools, spectral analysis/estimation subject to highly incomplete/sparse data, efficient high-dimensional functional representation and identification, stochastic/fractional calculus modeling and applications, nonlinear stochastic dynamics, stochastic stability and control theory, multi-scale/multi-physics stochastic modeling and analysis, stochastic model/dimension reduction techniques, Monte Carlo simulation methods, and risk/reliability assessment applications.
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