MS093 - Emerging Trends in Model Reduction for Nonlinear Mechanics Problems
Keywords: Data-driven model reduction, Invariant manifolds, Model order reduction, Nonlinear finite elements, Nonlinear model reduction, Nonlinear normal modes, Spectral submanifolds, Computational mechanics
The physical processes of interest to contemporary science and engineering are growing ever more complex. As a result, their governing equations are becoming high-dimensional or even partially unknown. For the efficient analysis, prediction, design, uncertainty quantification and control of these processes, reduced-order models capturing the core of the underlying physical phenomena are a must. This minisymposium focuses on the recent developments in computational methods and tools for rigorous model reduction for nonlinear computational mechanics problems. In addition to the advances in classic projection-based reduced-order models, this symposium aims to highlight modern reduction methods that leverage dynamical systems theory of invariant manifolds for equation-driven reduced-order modelling on the one hand, and dynamics/mechanics-informed machine learning methods for data-driven model reduction on the other hand. The speakers at this minisymposium will show applications of data-driven as well as equation-driven model reduction methods in various fields of nonlinear computational mechanics such as structural dynamics, fluid mechanics, fluid-structure interaction, micro-electromechanical systems, soft robots among others.